{"columns":[{"name":"objective_name","type":"string"},{"name":"solver_name","type":"string"},{"name":"data_name","type":"string"},{"name":"idx_rep","type":"int64"},{"name":"sampling_strategy","type":"string"},{"name":"obj_description","type":"string"},{"name":"solver_description","type":"string"},{"name":"stop_val","type":"int64"},{"name":"time","type":"double"},{"name":"objective_value","type":"double"},{"name":"objective_average_test_loss","type":"double"},{"name":"objective_average_test_metric","type":"double"},{"name":"objective_average_train_loss","type":"double"},{"name":"objective_client_test_0_test_metric","type":"double"},{"name":"objective_client_test_1_test_metric","type":"double"},{"name":"objective_client_test_2_test_metric","type":"double"},{"name":"objective_client_test_3_test_metric","type":"double"},{"name":"objective_client_test_4_test_metric","type":"double"},{"name":"objective_client_test_5_test_metric","type":"double"},{"name":"objective_pooled_test_metric","type":"double"},{"name":"objective_test_loss_client_0","type":"double"},{"name":"objective_test_loss_client_1","type":"double"},{"name":"objective_test_loss_client_2","type":"double"},{"name":"objective_test_loss_client_3","type":"double"},{"name":"objective_test_loss_client_4","type":"double"},{"name":"objective_test_loss_client_5","type":"double"},{"name":"objective_train_loss_client_0","type":"double"},{"name":"objective_train_loss_client_1","type":"double"},{"name":"objective_train_loss_client_2","type":"double"},{"name":"objective_train_loss_client_3","type":"double"},{"name":"objective_train_loss_client_4","type":"double"},{"name":"objective_train_loss_client_5","type":"double"},{"name":"env-OMP_NUM_THREADS","type":"null"},{"name":"platform","type":"string"},{"name":"platform-architecture","type":"string"},{"name":"platform-release","type":"string"},{"name":"platform-version","type":"string"},{"name":"system-cpus","type":"int64"},{"name":"system-processor","type":"string"},{"name":"system-ram (GB)","type":"int64"},{"name":"version-cuda","type":"null"},{"name":"version-numpy","type":"list<element: string>"},{"name":"version-scipy","type":"string"},{"name":"benchmark-git-tag","type":"null"}],"n_rows":91,"rows_kept":91,"cut":false,"text_format":"parquet-jsonl-2026-10-02","bounds":{"document_bytes":16777216,"package_text_limit_bytes":33554432,"package_text_slack_bytes":1024,"rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096,"vector_min":64,"vector_head_fixed":16,"vector_head_variable":64,"kv_value_chars":1048576,"nesting_max":64,"binary_text":true},"cells_cut":0,"cells_bad_utf8":0,"cells_raw":0,"cells_binary":0,"cells_binary_text":0,"cells_vector":0,"n_row_groups":1,"reader":"pyarrow 23.0.1, python 3.13","created_by":"parquet-cpp-arrow version 16.1.0","schema":{"fields":[{"name":"objective_name","type":"string","nullable":true,"metadata":null,"children":null},{"name":"solver_name","type":"string","nullable":true,"metadata":null,"children":null},{"name":"data_name","type":"string","nullable":true,"metadata":null,"children":null},{"name":"idx_rep","type":"int64","nullable":true,"metadata":null,"children":null},{"name":"sampling_strategy","type":"string","nullable":true,"metadata":null,"children":null},{"name":"obj_description","type":"string","nullable":true,"metadata":null,"children":null},{"name":"solver_description","type":"string","nullable":true,"metadata":null,"children":null},{"name":"stop_val","type":"int64","nullable":true,"metadata":null,"children":null},{"name":"time","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_value","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_average_test_loss","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_average_test_metric","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_average_train_loss","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_client_test_0_test_metric","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_client_test_1_test_metric","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_client_test_2_test_metric","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_client_test_3_test_metric","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_client_test_4_test_metric","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_client_test_5_test_metric","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_pooled_test_metric","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_test_loss_client_0","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_test_loss_client_1","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_test_loss_client_2","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_test_loss_client_3","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_test_loss_client_4","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_test_loss_client_5","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_train_loss_client_0","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_train_loss_client_1","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_train_loss_client_2","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_train_loss_client_3","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_train_loss_client_4","type":"double","nullable":true,"metadata":null,"children":null},{"name":"objective_train_loss_client_5","type":"double","nullable":true,"metadata":null,"children":null},{"name":"env-OMP_NUM_THREADS","type":"null","nullable":true,"metadata":null,"children":null},{"name":"platform","type":"string","nullable":true,"metadata":null,"children":null},{"name":"platform-architecture","type":"string","nullable":true,"metadata":null,"children":null},{"name":"platform-release","type":"string","nullable":true,"metadata":null,"children":null},{"name":"platform-version","type":"string","nullable":true,"metadata":null,"children":null},{"name":"system-cpus","type":"int64","nullable":true,"metadata":null,"children":null},{"name":"system-processor","type":"string","nullable":true,"metadata":null,"children":null},{"name":"system-ram (GB)","type":"int64","nullable":true,"metadata":null,"children":null},{"name":"version-cuda","type":"null","nullable":true,"metadata":null,"children":null},{"name":"version-numpy","type":"list<element: string>","nullable":true,"metadata":null,"children":[{"name":"element","type":"string","nullable":true,"metadata":null,"children":null}]},{"name":"version-scipy","type":"string","nullable":true,"metadata":null,"children":null},{"name":"benchmark-git-tag","type":"null","nullable":true,"metadata":null,"children":null}],"parquet":"required group field_id=-1 schema {\n  optional binary field_id=-1 objective_name (String);\n  optional binary field_id=-1 solver_name (String);\n  optional binary field_id=-1 data_name (String);\n  optional int64 field_id=-1 idx_rep;\n  optional binary field_id=-1 sampling_strategy (String);\n  optional binary field_id=-1 obj_description (String);\n  optional binary field_id=-1 solver_description (String);\n  optional int64 field_id=-1 stop_val;\n  optional double field_id=-1 time;\n  optional double field_id=-1 objective_value;\n  optional double field_id=-1 objective_average_test_loss;\n  optional double field_id=-1 objective_average_test_metric;\n  optional double field_id=-1 objective_average_train_loss;\n  optional double field_id=-1 objective_client_test_0_test_metric;\n  optional double field_id=-1 objective_client_test_1_test_metric;\n  optional double field_id=-1 objective_client_test_2_test_metric;\n  optional double field_id=-1 objective_client_test_3_test_metric;\n  optional double field_id=-1 objective_client_test_4_test_metric;\n  optional double field_id=-1 objective_client_test_5_test_metric;\n  optional double field_id=-1 objective_pooled_test_metric;\n  optional double field_id=-1 objective_test_loss_client_0;\n  optional double field_id=-1 objective_test_loss_client_1;\n  optional double field_id=-1 objective_test_loss_client_2;\n  optional double field_id=-1 objective_test_loss_client_3;\n  optional double field_id=-1 objective_test_loss_client_4;\n  optional double field_id=-1 objective_test_loss_client_5;\n  optional double field_id=-1 objective_train_loss_client_0;\n  optional double field_id=-1 objective_train_loss_client_1;\n  optional double field_id=-1 objective_train_loss_client_2;\n  optional double field_id=-1 objective_train_loss_client_3;\n  optional double field_id=-1 objective_train_loss_client_4;\n  optional double field_id=-1 objective_train_loss_client_5;\n  optional int32 field_id=-1 env-OMP_NUM_THREADS (Null);\n  optional binary field_id=-1 platform (String);\n  optional binary field_id=-1 platform-architecture (String);\n  optional binary field_id=-1 platform-release (String);\n  optional binary field_id=-1 platform-version (String);\n  optional int64 field_id=-1 system-cpus;\n  optional binary field_id=-1 system-processor (String);\n  optional int64 field_id=-1 system-ram (GB);\n  optional int32 field_id=-1 version-cuda (Null);\n  optional group field_id=-1 version-numpy (List) {\n    repeated group field_id=-1 list {\n      optional binary field_id=-1 element (String);\n    }\n  }\n  optional binary field_id=-1 version-scipy (String);\n  optional int32 field_id=-1 benchmark-git-tag (Null);\n}\n"},"kv_metadata":[["ARROW:schema",{"$cut":{"chars":13048,"head":""}}],["_meta_json","{\"plots\": [\"objective_curve\", \"suboptimality_curve\", \"relative_suboptimality_curve\", \"bar_chart\"], \"plot_configs\": {}}"],["pandas","{\"index_columns\": [{\"kind\": \"range\", \"name\": null, \"start\": 0, \"stop\": 91, \"step\": 1}], \"column_indexes\": [{\"name\": null, \"field_name\": null, \"pandas_type\": \"unicode\", \"numpy_type\": \"object\", \"metadata\": {\"encoding\": \"UTF-8\"}}], \"columns\": [{\"name\": \"objective_name\", \"field_name\": \"objective_name\", \"pandas_type\": \"unicode\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"solver_name\", \"field_name\": \"solver_name\", \"pandas_type\": \"unicode\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"data_name\", \"field_name\": \"data_name\", \"pandas_type\": \"unicode\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"idx_rep\", \"field_name\": \"idx_rep\", \"pandas_type\": \"int64\", \"numpy_type\": \"int64\", \"metadata\": null}, {\"name\": \"sampling_strategy\", \"field_name\": \"sampling_strategy\", \"pandas_type\": \"unicode\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"obj_description\", \"field_name\": \"obj_description\", \"pandas_type\": \"unicode\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"solver_description\", \"field_name\": \"solver_description\", \"pandas_type\": \"unicode\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"stop_val\", \"field_name\": \"stop_val\", \"pandas_type\": \"int64\", \"numpy_type\": \"int64\", \"metadata\": null}, {\"name\": \"time\", \"field_name\": \"time\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_value\", \"field_name\": \"objective_value\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_average_test_loss\", \"field_name\": \"objective_average_test_loss\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_average_test_metric\", \"field_name\": \"objective_average_test_metric\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_average_train_loss\", \"field_name\": \"objective_average_train_loss\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_client_test_0_test_metric\", \"field_name\": \"objective_client_test_0_test_metric\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_client_test_1_test_metric\", \"field_name\": \"objective_client_test_1_test_metric\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_client_test_2_test_metric\", \"field_name\": \"objective_client_test_2_test_metric\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_client_test_3_test_metric\", \"field_name\": \"objective_client_test_3_test_metric\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_client_test_4_test_metric\", \"field_name\": \"objective_client_test_4_test_metric\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_client_test_5_test_metric\", \"field_name\": \"objective_client_test_5_test_metric\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_pooled_test_metric\", \"field_name\": \"objective_pooled_test_metric\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_test_loss_client_0\", \"field_name\": \"objective_test_loss_client_0\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_test_loss_client_1\", \"field_name\": \"objective_test_loss_client_1\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_test_loss_client_2\", \"field_name\": \"objective_test_loss_client_2\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_test_loss_client_3\", \"field_name\": \"objective_test_loss_client_3\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_test_loss_client_4\", \"field_name\": \"objective_test_loss_client_4\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_test_loss_client_5\", \"field_name\": \"objective_test_loss_client_5\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_train_loss_client_0\", \"field_name\": \"objective_train_loss_client_0\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_train_loss_client_1\", \"field_name\": \"objective_train_loss_client_1\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_train_loss_client_2\", \"field_name\": \"objective_train_loss_client_2\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_train_loss_client_3\", \"field_name\": \"objective_train_loss_client_3\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_train_loss_client_4\", \"field_name\": \"objective_train_loss_client_4\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"objective_train_loss_client_5\", \"field_name\": \"objective_train_loss_client_5\", \"pandas_type\": \"float64\", \"numpy_type\": \"float64\", \"metadata\": null}, {\"name\": \"env-OMP_NUM_THREADS\", \"field_name\": \"env-OMP_NUM_THREADS\", \"pandas_type\": \"empty\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"platform\", \"field_name\": \"platform\", \"pandas_type\": \"unicode\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"platform-architecture\", \"field_name\": \"platform-architecture\", \"pandas_type\": \"unicode\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"platform-release\", \"field_name\": \"platform-release\", \"pandas_type\": \"unicode\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"platform-version\", \"field_name\": \"platform-version\", \"pandas_type\": \"unicode\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"system-cpus\", \"field_name\": \"system-cpus\", \"pandas_type\": \"int64\", \"numpy_type\": \"int64\", \"metadata\": null}, {\"name\": \"system-processor\", \"field_name\": \"system-processor\", \"pandas_type\": \"unicode\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"system-ram (GB)\", \"field_name\": \"system-ram (GB)\", \"pandas_type\": \"int64\", \"numpy_type\": \"int64\", \"metadata\": null}, {\"name\": \"version-cuda\", \"field_name\": \"version-cuda\", \"pandas_type\": \"empty\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"version-numpy\", \"field_name\": \"version-numpy\", \"pandas_type\": \"list[unicode]\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"version-scipy\", \"field_name\": \"version-scipy\", \"pandas_type\": \"unicode\", \"numpy_type\": \"object\", \"metadata\": null}, {\"name\": \"benchmark-git-tag\", \"field_name\": \"benchmark-git-tag\", \"pandas_type\": \"empty\", \"numpy_type\": \"object\", \"metadata\": null}], \"creator\": {\"library\": \"pyarrow\", \"version\": \"16.1.0\"}, \"pandas_version\": \"2.2.2\"}"]]}
["FLamby[seed=42]","Cyclic[batch_size=32,deterministic_cycle=True,learning_rate=0.0031622776601683794,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Cyclic and RandomWalk \"FL\" strategies.\n\nThis solver uses FLamby's implementation of the Cyclical\nweight transfer, which is successively fine-tuning a model.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://pubmed.ncbi.nlm.nih.gov/29617797/",0,0.00275717399927089,0.4150216299523082,0.36538801590601605,0.6984627466471042,0.4150216299523082,0.5670391061452514,0.8181818181818182,0.7222222222222222,0.4166666666666667,1.0,0.6666666666666666,0.6923076923076923,0.8838081359863281,0.18267583847045898,0.5217576026916504,0.413883239030838,0.09549399465322495,0.09470928460359573,0.6022377709547678,0.8673385679721832,0.4092247486114502,0.4264897406101227,0.18259868025779724,0.002240271307528019,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Cyclic[batch_size=32,deterministic_cycle=True,learning_rate=0.0031622776601683794,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Cyclic and RandomWalk \"FL\" strategies.\n\nThis solver uses FLamby's implementation of the Cyclical\nweight transfer, which is successively fine-tuning a model.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://pubmed.ncbi.nlm.nih.gov/29617797/",10,7.418683501990017,0.3546516885463562,0.30499618748823804,0.6865802323724587,0.3546516885463562,0.553072625698324,0.8181818181818182,0.6666666666666666,0.5,0.9148936170212766,0.6666666666666666,0.6827838827838828,0.6852610111236572,0.19381625950336456,0.4301021099090576,0.3016197085380554,0.13498525321483612,0.08419278264045715,0.529652069012324,0.7311400473117828,0.3320745974779129,0.3789462596178055,0.15065888315439224,0.0054382747039198875,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Cyclic[batch_size=32,deterministic_cycle=True,learning_rate=0.0031622776601683794,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Cyclic and RandomWalk \"FL\" strategies.\n\nThis solver uses FLamby's implementation of the Cyclical\nweight transfer, which is successively fine-tuning a model.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://pubmed.ncbi.nlm.nih.gov/29617797/",20,14.620260654985032,0.3520474391472008,0.30221566185355186,0.6985471710314712,0.3520474391472008,0.5642458100558659,0.8181818181818182,0.6851851851851852,0.5208333333333334,0.9361702127659575,0.6666666666666666,0.6937728937728938,0.6762588024139404,0.19409248232841492,0.42955678701400757,0.2988344728946686,0.13388538360595703,0.08066604286432266,0.520562211672465,0.7285269200801849,0.33133751153945923,0.37827523797750473,0.14830612391233444,0.005276629701256752,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Cyclic[batch_size=32,deterministic_cycle=True,learning_rate=0.0031622776601683794,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Cyclic and RandomWalk \"FL\" strategies.\n\nThis solver uses FLamby's implementation of the Cyclical\nweight transfer, which is successively fine-tuning a model.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://pubmed.ncbi.nlm.nih.gov/29617797/",30,21.598377112983144,0.3494981710229897,0.29997135947148007,0.6955496989187409,0.3494981710229897,0.5810055865921788,0.7954545454545454,0.6944444444444444,0.5208333333333334,0.9148936170212766,0.6666666666666666,0.6981684981684981,0.6668508648872375,0.19631147384643555,0.42791295051574707,0.293250173330307,0.13617998361587524,0.0793227106332779,0.515982468922933,0.7233397364616394,0.32856084406375885,0.37712641060352325,0.14655107259750366,0.0054284934885799885,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Cyclic[batch_size=32,deterministic_cycle=True,learning_rate=0.0031622776601683794,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Cyclic and RandomWalk \"FL\" strategies.\n\nThis solver uses FLamby's implementation of the Cyclical\nweight transfer, which is successively fine-tuning a model.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://pubmed.ncbi.nlm.nih.gov/29617797/",40,28.968202499982,0.34744680145134527,0.2976792926589648,0.7075199685284773,0.34744680145134527,0.6033519553072626,0.7727272727272727,0.7037037037037037,0.5625,0.9361702127659575,0.6666666666666666,0.7106227106227107,0.6595059633255005,0.1964651495218277,0.4277321994304657,0.29125502705574036,0.13492506742477417,0.07619234919548035,0.5082433223724365,0.7215989232063293,0.3282892107963562,0.3766724467277527,0.14462660998106003,0.005250295624136925,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Cyclic[batch_size=32,deterministic_cycle=True,learning_rate=0.0031622776601683794,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Cyclic and RandomWalk \"FL\" strategies.\n\nThis solver uses FLamby's implementation of the Cyclical\nweight transfer, which is successively fine-tuning a model.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://pubmed.ncbi.nlm.nih.gov/29617797/",50,36.18067521798366,0.3456307338136766,0.2956305568416913,0.7120030363262564,0.3456307338136766,0.611731843575419,0.7727272727272727,0.7222222222222222,0.5625,0.9361702127659575,0.6666666666666666,0.7216117216117216,0.652972936630249,0.19664433598518372,0.42763328552246094,0.28946101665496826,0.1337602734565735,0.07331149280071259,0.5012711485226949,0.7201315462589264,0.3281249403953552,0.37628450989723206,0.1428910568356514,0.00508120097219944,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Cyclic[batch_size=32,deterministic_cycle=True,learning_rate=0.0031622776601683794,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Cyclic and RandomWalk \"FL\" strategies.\n\nThis solver uses FLamby's implementation of the Cyclical\nweight transfer, which is successively fine-tuning a model.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://pubmed.ncbi.nlm.nih.gov/29617797/",60,43.15618374897895,0.34582001630527276,0.29545602947473526,0.7684896905782738,0.34582001630527276,0.61731843575419,0.7727272727272727,0.7222222222222222,0.5625,0.9361702127659575,1.0,0.7256410256410256,0.6543611288070679,0.19510303437709808,0.42860159277915955,0.2918991446495056,0.13106907904148102,0.07170219719409943,0.49828487634658813,0.722574770450592,0.3299417495727539,0.37674176692962646,0.14254207164049149,0.004834862891584635,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Cyclic[batch_size=32,deterministic_cycle=True,learning_rate=0.0031622776601683794,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Cyclic and RandomWalk \"FL\" strategies.\n\nThis solver uses FLamby's implementation of the Cyclical\nweight transfer, which is successively fine-tuning a model.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://pubmed.ncbi.nlm.nih.gov/29617797/",70,50.52120136098165,0.34419839516178596,0.2936578504741192,0.7733585608451886,0.34419839516178596,0.6256983240223464,0.7727272727272727,0.7222222222222222,0.5833333333333334,0.9361702127659575,1.0,0.736996336996337,0.648540735244751,0.19547776877880096,0.4284612536430359,0.2900104224681854,0.13027173280715942,0.06918518990278244,0.4922934373219808,0.7211383581161499,0.3297004997730255,0.37636642158031464,0.14099108427762985,0.004700569901615381,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Cyclic[batch_size=32,deterministic_cycle=True,learning_rate=0.0031622776601683794,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Cyclic and RandomWalk \"FL\" strategies.\n\nThis solver uses FLamby's implementation of the Cyclical\nweight transfer, which is successively fine-tuning a model.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://pubmed.ncbi.nlm.nih.gov/29617797/",80,57.74132321097568,0.3427685423650675,0.2920529978970687,0.777082955631036,0.3427685423650675,0.6480446927374302,0.7727272727272727,0.7222222222222222,0.5833333333333334,0.9361702127659575,1.0,0.7472527472527473,0.64335036277771,0.19586332142353058,0.42838823795318604,0.2883074879646301,0.1295369416475296,0.06687163561582565,0.4869070549805959,0.7199371457099915,0.32954950630664825,0.3760483041405678,0.139597088098526,0.004572154954075813,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Cyclic[batch_size=32,deterministic_cycle=True,learning_rate=0.0031622776601683794,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Cyclic and RandomWalk \"FL\" strategies.\n\nThis solver uses FLamby's implementation of the Cyclical\nweight transfer, which is successively fine-tuning a model.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://pubmed.ncbi.nlm.nih.gov/29617797/",90,64.74842905097103,0.34138238370522034,0.29073243339856464,0.7774019431474164,0.34138238370522034,0.659217877094972,0.7727272727272727,0.7129629629629629,0.5833333333333334,0.9361702127659575,1.0,0.7531135531135531,0.6375294327735901,0.1972951740026474,0.4277041256427765,0.2851307690143585,0.13075634837150574,0.0659787505865097,0.4842794934908549,0.7171341478824615,0.32814669609069824,0.37545574456453323,0.13865453004837036,0.004623690154403448,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Cyclic[batch_size=32,deterministic_cycle=True,learning_rate=0.0031622776601683794,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Cyclic and RandomWalk \"FL\" strategies.\n\nThis solver uses FLamby's implementation of the Cyclical\nweight transfer, which is successively fine-tuning a model.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://pubmed.ncbi.nlm.nih.gov/29617797/",100,72.16114537796966,0.34030121518298984,0.2894505336880684,0.7792641405403401,0.34030121518298984,0.6703910614525139,0.7727272727272727,0.7129629629629629,0.5833333333333334,0.9361702127659575,1.0,0.756043956043956,0.6335419416427612,0.19754073023796082,0.42781567573547363,0.28398874402046204,0.12990333139896393,0.0639127790927887,0.479711651802063,0.7165318131446838,0.328287735581398,0.375273160636425,0.13751666247844696,0.004486267454922199,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Cyclic[batch_size=32,deterministic_cycle=True,learning_rate=0.0031622776601683794,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Cyclic and RandomWalk \"FL\" strategies.\n\nThis solver uses FLamby's implementation of the Cyclical\nweight transfer, which is successively fine-tuning a model.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://pubmed.ncbi.nlm.nih.gov/29617797/",110,79.42999832296118,0.33935853026600354,0.28831479512155056,0.7871467457970489,0.33935853026600354,0.6871508379888268,0.7727272727272727,0.7222222222222222,0.5833333333333334,0.9574468085106383,1.0,0.7637362637362637,0.6300328373908997,0.19779738783836365,0.4279569983482361,0.28297486901283264,0.12911733984947205,0.06200933828949928,0.47559566299120587,0.7160961329936981,0.3284796476364136,0.37512975186109543,0.13649431616067886,0.00435566995292902,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Cyclic[batch_size=32,deterministic_cycle=True,learning_rate=0.0031622776601683794,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Cyclic and RandomWalk \"FL\" strategies.\n\nThis solver uses FLamby's implementation of the Cyclical\nweight transfer, which is successively fine-tuning a model.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://pubmed.ncbi.nlm.nih.gov/29617797/",120,86.51001006495426,0.339442008614747,0.28821675293147564,0.78761229514528,0.339442008614747,0.6899441340782123,0.7727272727272727,0.7222222222222222,0.5833333333333334,0.9574468085106383,1.0,0.7652014652014651,0.630963146686554,0.19705519080162048,0.4284670650959015,0.2841658294200897,0.12766794860363007,0.06098133698105812,0.4737723072369893,0.7175379991531372,0.3295283913612366,0.37538254261016846,0.13621170818805695,0.004219103138893843,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdagrad[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.03162277660168379,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdagrad FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adagrad solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf\n- https://arxiv.org/abs/2003.00295",0,0.0024381219991482794,0.4150216299523082,0.36538801590601605,0.6984627466471042,0.4150216299523082,0.5670391061452514,0.8181818181818182,0.7222222222222222,0.4166666666666667,1.0,0.6666666666666666,0.6923076923076923,0.8838081359863281,0.18267583847045898,0.5217576026916504,0.413883239030838,0.09549399465322495,0.09470928460359573,0.6022377709547678,0.8673385679721832,0.4092247486114502,0.4264897406101227,0.18259868025779724,0.002240271307528019,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdagrad[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.03162277660168379,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdagrad FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adagrad solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf\n- https://arxiv.org/abs/2003.00295",10,43.571937468001124,0.3536311261640448,0.30134424070517224,0.7165629202667616,0.3536311261640448,0.6005586592178771,0.8409090909090909,0.7129629629629629,0.5208333333333334,0.9574468085106383,0.6666666666666666,0.7230769230769231,0.6888399124145508,0.18759456276893616,0.42423611879348755,0.3080975413322449,0.12118527293205261,0.07811203598976135,0.527276893456777,0.7231887578964233,0.3337225914001465,0.38186847418546677,0.1522163301706314,0.0035137098748236895,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdagrad[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.03162277660168379,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdagrad FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adagrad solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf\n- https://arxiv.org/abs/2003.00295",20,87.34817830300744,0.33252468671546215,0.2873578729728858,0.7821530378337305,0.33252468671546215,0.7681564245810056,0.5681818181818182,0.7129629629629629,0.75,0.8936170212765957,1.0,0.7893772893772893,0.6003382802009583,0.22675339877605438,0.417990505695343,0.24741815030574799,0.15640504658222198,0.07524185627698898,0.5155421098073324,0.6547766625881195,0.2999155968427658,0.3784238249063492,0.14163200557231903,0.004857920575886965,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdagrad[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.03162277660168379,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdagrad FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adagrad solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf\n- https://arxiv.org/abs/2003.00295",30,130.96480381200308,0.32861540582962334,0.28870486343900365,0.7650683706193037,0.32861540582962334,0.8212290502793296,0.5227272727272727,0.6666666666666666,0.75,0.8297872340425532,1.0,0.7901098901098901,0.5815420150756836,0.2436799556016922,0.42620423436164856,0.237967848777771,0.1662573367357254,0.07657779008150101,0.5116825699806213,0.6391234993934631,0.2936984896659851,0.3821021318435669,0.14046421647071838,0.004621527623385191,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdagrad[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.03162277660168379,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdagrad FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adagrad solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf\n- https://arxiv.org/abs/2003.00295",40,174.3594034230009,0.3226309234224674,0.2810240052640438,0.777334702966909,0.3226309234224674,0.8072625698324022,0.5681818181818182,0.75,0.6875,0.851063829787234,1.0,0.8095238095238095,0.5687087178230286,0.23358957469463348,0.41159385442733765,0.24570077657699585,0.15573744475841522,0.07081366330385208,0.4934475819269816,0.6333716213703156,0.29077984392642975,0.37592925131320953,0.1386009231209755,0.0036563188768923283,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdagrad[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.03162277660168379,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdagrad FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adagrad solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf\n- https://arxiv.org/abs/2003.00295",50,217.87734822799757,0.31907067313376397,0.2750556754569213,0.788998525110049,0.31907067313376397,0.8016759776536313,0.6363636363636364,0.7777777777777778,0.6458333333333334,0.8723404255319149,1.0,0.8179487179487179,0.5627090930938721,0.22441589832305908,0.3982643485069275,0.2521466910839081,0.14641842246055603,0.0663795992732048,0.48124294479688007,0.6327299177646637,0.2887315899133682,0.37082065641880035,0.13786505907773972,0.003033870831131935,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdagrad[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.03162277660168379,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdagrad FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adagrad solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf\n- https://arxiv.org/abs/2003.00295",60,261.23602511300487,0.31657774448912174,0.2716239020228386,0.7971834547917197,0.31657774448912174,0.8156424581005587,0.6136363636363636,0.8148148148148148,0.6666666666666666,0.8723404255319149,1.0,0.8263736263736263,0.5552281737327576,0.2230949103832245,0.3915083706378937,0.2519280016422272,0.14351914823055267,0.06446480751037598,0.4752364655335744,0.630066454410553,0.2855624780058861,0.368347205221653,0.13746248185634613,0.0027913819067180157,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdagrad[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.03162277660168379,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdagrad FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adagrad solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf\n- https://arxiv.org/abs/2003.00295",70,304.58919422300096,0.3145415037611706,0.2697395694752534,0.8029834237550966,0.3145415037611706,0.8296089385474861,0.6136363636363636,0.8148148148148148,0.6875,0.8723404255319149,1.0,0.8300366300366301,0.5463919043540955,0.22610196471214294,0.3893120586872101,0.24918772280216217,0.14388130605220795,0.06356246024370193,0.472239484389623,0.6254853308200836,0.2823086902499199,0.36760832369327545,0.13691800832748413,0.0026891850866377354,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdagrad[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.03162277660168379,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdagrad FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adagrad solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf\n- https://arxiv.org/abs/2003.00295",80,347.91425795799296,0.3130230522429985,0.2686054768661658,0.8134071525375658,0.3130230522429985,0.840782122905028,0.6136363636363636,0.8240740740740741,0.7083333333333334,0.8936170212765957,1.0,0.836996336996337,0.5394187569618225,0.2291119545698166,0.3886195719242096,0.24771231412887573,0.1441478729248047,0.06262239068746567,0.47022823492685956,0.6214274168014526,0.2800953909754753,0.36751437932252884,0.13629042357206345,0.002582467859610915,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdagrad[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.03162277660168379,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdagrad FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adagrad solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf\n- https://arxiv.org/abs/2003.00295",90,391.1539052049993,0.31182005516408634,0.2675185352563858,0.8120935823226921,0.31182005516408634,0.8463687150837989,0.5909090909090909,0.8333333333333334,0.7083333333333334,0.8936170212765957,1.0,0.8377289377289378,0.5345818996429443,0.23051373660564423,0.38745588064193726,0.2477770745754242,0.14335256814956665,0.06143005192279816,0.4682023525238037,0.618579238653183,0.2787405848503113,0.36723777651786804,0.1357046440243721,0.0024557344149798155,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdagrad[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.03162277660168379,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdagrad FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adagrad solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf\n- https://arxiv.org/abs/2003.00295",100,434.4496071800022,0.3108004175333513,0.2663704212754965,0.8139524487648918,0.3108004175333513,0.8463687150837989,0.6136363636363636,0.8425925925925926,0.6875,0.8936170212765957,1.0,0.836996336996337,0.5309219360351562,0.23090866208076477,0.38574862480163574,0.24825333058834076,0.14211969077587128,0.0602702833712101,0.4662073453267415,0.6165674030780792,0.27774327993392944,0.3666626363992691,0.13528355211019516,0.002338288351893425,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdagrad[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.03162277660168379,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdagrad FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adagrad solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf\n- https://arxiv.org/abs/2003.00295",110,477.70076982200044,0.3099112922920742,0.26530094879368943,0.8147941996066427,0.3099112922920742,0.8463687150837989,0.5909090909090909,0.8703703703703703,0.6875,0.8936170212765957,1.0,0.8388278388278388,0.5276767015457153,0.2311898022890091,0.384060263633728,0.24845772981643677,0.1410902440547943,0.05933095142245293,0.46448763211568195,0.6148692667484283,0.27684445679187775,0.3660014346241951,0.1350226104259491,0.002242353046312928,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdagrad[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.03162277660168379,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdagrad FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adagrad solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf\n- https://arxiv.org/abs/2003.00295",120,520.9888451220068,0.3091310158682366,0.26439513514439267,0.8157252983031045,0.3091310158682366,0.8519553072625698,0.5909090909090909,0.8703703703703703,0.6875,0.8936170212765957,1.0,0.8395604395604396,0.5246355533599854,0.23168031871318817,0.38266709446907043,0.24841615557670593,0.14039020240306854,0.05858148634433746,0.4630988538265228,0.6132400631904602,0.27602478861808777,0.3654187470674515,0.1348428651690483,0.00216077733784914,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdam[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdam FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adam solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1412.6980\n- https://arxiv.org/abs/2003.00295",0,0.002432981000310974,0.4150216299523082,0.36538801590601605,0.6984627466471042,0.4150216299523082,0.5670391061452514,0.8181818181818182,0.7222222222222222,0.4166666666666667,1.0,0.6666666666666666,0.6923076923076923,0.8838081359863281,0.18267583847045898,0.5217576026916504,0.413883239030838,0.09549399465322495,0.09470928460359573,0.6022377709547678,0.8673385679721832,0.4092247486114502,0.4264897406101227,0.18259868025779724,0.002240271307528019,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdam[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdam FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adam solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1412.6980\n- https://arxiv.org/abs/2003.00295",10,43.330263382009434,0.35357444917058783,0.3012893771131833,0.7170284696149926,0.35357444917058783,0.6033519553072626,0.8409090909090909,0.7129629629629629,0.5208333333333334,0.9574468085106383,0.6666666666666666,0.7234432234432234,0.6886380314826965,0.18762409687042236,0.424165278673172,0.30797767639160156,0.12123160064220428,0.0780995786190033,0.5272199710210165,0.7230445444583893,0.33364610373973846,0.3818354159593582,0.1521846428513527,0.0035160169936716557,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdam[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdam FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adam solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1412.6980\n- https://arxiv.org/abs/2003.00295",20,86.63076486799764,0.3325129693580999,0.28741515055298805,0.7790666180806439,0.3325129693580999,0.7681564245810056,0.5681818181818182,0.6944444444444444,0.75,0.8936170212765957,1.0,0.789010989010989,0.6001560688018799,0.22704946994781494,0.4181760251522064,0.2471829652786255,0.15663078427314758,0.075295589864254,0.5156563719113668,0.6546046137809753,0.2998270243406296,0.37850406765937805,0.14161967486143112,0.004866063594818115,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdam[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdam FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adam solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1412.6980\n- https://arxiv.org/abs/2003.00295",30,129.86240623599588,0.3285837448719475,0.28873294219374657,0.7650683706193037,0.3285837448719475,0.8212290502793296,0.5227272727272727,0.6666666666666666,0.75,0.8297872340425532,1.0,0.7897435897435897,0.5813573598861694,0.2438831627368927,0.42630839347839355,0.23786599934101105,0.16637130081653595,0.07661143690347672,0.5116725464661916,0.638974130153656,0.2936296984553337,0.3821546882390976,0.14045464247465134,0.0046167634427547455,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdam[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdam FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adam solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1412.6980\n- https://arxiv.org/abs/2003.00295",40,173.23504326199327,0.3225230835119469,0.28088275839885074,0.777334702966909,0.3225230835119469,0.8072625698324022,0.5681818181818182,0.75,0.6875,0.851063829787234,1.0,0.8095238095238095,0.5684513449668884,0.23344676196575165,0.4113241732120514,0.2458215206861496,0.15555304288864136,0.07069970667362213,0.4931456347306569,0.6332511305809021,0.29070958495140076,0.3758353441953659,0.1385614573955536,0.003635349217802286,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdam[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdam FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adam solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1412.6980\n- https://arxiv.org/abs/2003.00295",50,216.64553984799568,0.3189619115114005,0.27488092829783756,0.7894640744582798,0.3189619115114005,0.8044692737430168,0.6363636363636364,0.7777777777777778,0.6458333333333334,0.8723404255319149,1.0,0.8194139194139194,0.5624894499778748,0.22421593964099884,0.3978748917579651,0.25229501724243164,0.14617107808589935,0.06623919308185577,0.48091329137484234,0.6326665282249451,0.28864598274230957,0.3706974387168884,0.13783692568540573,0.003011302324011922,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdam[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdam FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adam solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1412.6980\n- https://arxiv.org/abs/2003.00295",60,260.13353020999784,0.3164423178208785,0.2714507964750131,0.7971834547917197,0.3164423178208785,0.8156424581005587,0.6136363636363636,0.8148148148148148,0.6666666666666666,0.8723404255319149,1.0,0.826007326007326,0.5547825694084167,0.22309839725494385,0.3911781311035156,0.2518770694732666,0.14341016113758087,0.06435845047235489,0.4749537209669749,0.6298650503158569,0.28537993133068085,0.3682457134127617,0.1374349519610405,0.0027745389379560947,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdam[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdam FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adam solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1412.6980\n- https://arxiv.org/abs/2003.00295",70,304.13200361999407,0.3143874351064571,0.2695939081410567,0.8029834237550966,0.3143874351064571,0.8296089385474861,0.6136363636363636,0.8148148148148148,0.6875,0.8723404255319149,1.0,0.8307692307692308,0.5457329154014587,0.22632765769958496,0.389117568731308,0.24900968372821808,0.14389680325984955,0.06347882002592087,0.47201405962308246,0.6251350045204163,0.2820633798837662,0.36755598336458206,0.13688092678785324,0.00267525645904243,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdam[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdam FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adam solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1412.6980\n- https://arxiv.org/abs/2003.00295",80,347.9524286009946,0.3128627515656667,0.26846886798739433,0.814950362414109,0.3128627515656667,0.840782122905028,0.6136363636363636,0.8333333333333334,0.7083333333333334,0.8936170212765957,1.0,0.8373626373626374,0.5387121438980103,0.2293863594532013,0.38847213983535767,0.24759037792682648,0.14414146542549133,0.06251072138547897,0.47000373403231305,0.6210302710533142,0.27985595166683197,0.3674837723374367,0.13623696565628052,0.002565814647823572,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdam[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdam FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adam solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1412.6980\n- https://arxiv.org/abs/2003.00295",90,391.5483373969946,0.3116506066225055,0.26735248416662216,0.8120935823226921,0.3116506066225055,0.8463687150837989,0.5909090909090909,0.8333333333333334,0.7083333333333334,0.8936170212765957,1.0,0.8384615384615385,0.5338971614837646,0.23070624470710754,0.38723623752593994,0.24776791036128998,0.14324040710926056,0.0612669438123703,0.4679269691308339,0.6181974411010742,0.27852436155080795,0.36718034744262695,0.1356395035982132,0.0024350169114768505,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdam[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdam FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adam solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1412.6980\n- https://arxiv.org/abs/2003.00295",100,435.26816660698387,0.3106199791525594,0.2661691444615523,0.8139524487648918,0.3106199791525594,0.8463687150837989,0.6136363636363636,0.8425925925925926,0.6875,0.8936170212765957,1.0,0.8362637362637363,0.5302315354347229,0.23103566467761993,0.3854432702064514,0.24828790128231049,0.1419384479522705,0.06007804721593857,0.4658900896708171,0.6161962449550629,0.27753229439258575,0.36656343936920166,0.13522210717201233,0.00231569935567677,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdam[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdam FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adam solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1412.6980\n- https://arxiv.org/abs/2003.00295",110,478.68226324899297,0.3097194659575406,0.26508048673470813,0.8132509897300993,0.3097194659575406,0.8463687150837989,0.5909090909090909,0.8611111111111112,0.6875,0.8936170212765957,1.0,0.8377289377289378,0.5269272327423096,0.2313269078731537,0.3837200701236725,0.24846673011779785,0.1409073919057846,0.0591345876455307,0.46415801843007404,0.6144700944423676,0.2766227349638939,0.36587484180927277,0.1349717453122139,0.002219360787421465,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedAdam[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAdam FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Adam solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1412.6980\n- https://arxiv.org/abs/2003.00295",120,522.0236288849919,0.308929409366101,0.26417036168277264,0.8172685081796477,0.308929409366101,0.8519553072625698,0.5909090909090909,0.8796296296296297,0.6875,0.8936170212765957,1.0,0.8402930402930403,0.5238155722618103,0.2318662852048874,0.38232702016830444,0.24839794635772705,0.14023059606552124,0.05838475003838539,0.46277302503585815,0.6127897202968597,0.2757957801222801,0.365282878279686,0.13479819893836975,0.0021368535235524178,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FederatedAveraging[batch_size=32,learning_rate=0.01,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAvg strategy.\n\nThis solver uses FLamby's implementation of FedAvg.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1602.05629",0,0.0022422440015361644,0.4150216299523082,0.36538801590601605,0.6984627466471042,0.4150216299523082,0.5670391061452514,0.8181818181818182,0.7222222222222222,0.4166666666666667,1.0,0.6666666666666666,0.6923076923076923,0.8838081359863281,0.18267583847045898,0.5217576026916504,0.413883239030838,0.09549399465322495,0.09470928460359573,0.6022377709547678,0.8673385679721832,0.4092247486114502,0.4264897406101227,0.18259868025779724,0.002240271307528019,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FederatedAveraging[batch_size=32,learning_rate=0.01,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAvg strategy.\n\nThis solver uses FLamby's implementation of FedAvg.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1602.05629",10,43.49968005600749,0.3335834781432317,0.2887819620470206,0.7746742530299229,0.3335834781432317,0.7067039106145251,0.75,0.6944444444444444,0.6458333333333334,0.851063829787234,1.0,0.7666666666666667,0.6099698543548584,0.21501335501670837,0.4250078797340393,0.25528478622436523,0.15377642214298248,0.07363947480916977,0.5042812625567118,0.6725933849811554,0.3069213926792145,0.37465800344944,0.1370728835463524,0.005973941646516323,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FederatedAveraging[batch_size=32,learning_rate=0.01,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAvg strategy.\n\nThis solver uses FLamby's implementation of FedAvg.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1602.05629",20,86.95967590999498,0.32415038298091126,0.27786189193526906,0.811477748315331,0.32415038298091126,0.8100558659217877,0.7045454545454546,0.7314814814814815,0.7291666666666666,0.8936170212765957,1.0,0.8245421245421245,0.5750035047531128,0.21592088043689728,0.420479416847229,0.24690695106983185,0.1473768949508667,0.061483703553676605,0.4830439289410909,0.6513860523700714,0.3020137548446655,0.3731749653816223,0.13058368116617203,0.0046999151818454266,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FederatedAveraging[batch_size=32,learning_rate=0.01,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAvg strategy.\n\nThis solver uses FLamby's implementation of FedAvg.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1602.05629",30,130.70174271699216,0.31847049253216636,0.27083265284697217,0.8226025359182377,0.31847049253216636,0.8324022346368715,0.6818181818181818,0.7777777777777778,0.75,0.8936170212765957,1.0,0.8402930402930403,0.5543595552444458,0.21627826988697052,0.4151301681995392,0.24278664588928223,0.14245295524597168,0.053988322615623474,0.47134071588516235,0.6380147933959961,0.29887881875038147,0.371813602745533,0.12693197652697563,0.0038430478889495134,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FederatedAveraging[batch_size=32,learning_rate=0.01,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAvg strategy.\n\nThis solver uses FLamby's implementation of FedAvg.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1602.05629",40,174.47829302198807,0.31476137029110557,0.2662721474965413,0.8248350649941442,0.31476137029110557,0.8379888268156425,0.6590909090909091,0.7870370370370371,0.75,0.9148936170212766,1.0,0.8454212454212454,0.5405600666999817,0.21754059195518494,0.4103409945964813,0.2396714985370636,0.1399070918560028,0.04961264133453369,0.46502093474070233,0.6284443736076355,0.29613184928894043,0.3707674890756607,0.12490073963999748,0.0033028353936970234,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FederatedAveraging[batch_size=32,learning_rate=0.01,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAvg strategy.\n\nThis solver uses FLamby's implementation of FedAvg.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1602.05629",50,218.41428428599465,0.3121620031047819,0.2630856229613225,0.8276738354395947,0.3121620031047819,0.835195530726257,0.6363636363636364,0.7870370370370371,0.75,0.9574468085106383,1.0,0.845054945054945,0.5307644009590149,0.21890132129192352,0.4059194028377533,0.2374505251646042,0.13850954174995422,0.04696854576468468,0.46127258737881977,0.6213917136192322,0.2937241494655609,0.3698894754052162,0.12376638129353523,0.0029277114663273096,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FederatedAveraging[batch_size=32,learning_rate=0.01,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAvg strategy.\n\nThis solver uses FLamby's implementation of FedAvg.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1602.05629",60,262.6204071909997,0.3102282972023305,0.260525144636631,0.8276738354395947,0.3102282972023305,0.835195530726257,0.6363636363636364,0.7870370370370371,0.75,0.9574468085106383,1.0,0.845054945054945,0.5243629217147827,0.2190883904695511,0.4010532796382904,0.23681923747062683,0.13673965632915497,0.045087382197380066,0.4583095709482829,0.6165879964828491,0.29190973937511444,0.3688920959830284,0.12305788695812225,0.0026124934665858746,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FederatedAveraging[batch_size=32,learning_rate=0.01,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAvg strategy.\n\nThis solver uses FLamby's implementation of FedAvg.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1602.05629",70,306.6222661560023,0.30873345037818783,0.25866060083111125,0.8308433330225004,0.30873345037818783,0.8491620111731844,0.6136363636363636,0.8148148148148148,0.75,0.9574468085106383,1.0,0.847985347985348,0.5189743041992188,0.2199096977710724,0.39695972204208374,0.23599204421043396,0.1360628306865692,0.04406500607728958,0.4564699133237203,0.6126190423965454,0.29006876796483994,0.3681096136569977,0.12274223938584328,0.0023911255411803722,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FederatedAveraging[batch_size=32,learning_rate=0.01,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAvg strategy.\n\nThis solver uses FLamby's implementation of FedAvg.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1602.05629",80,350.5693450399958,0.3075380123272124,0.257208530480663,0.8378616545355025,0.3075380123272124,0.8491620111731844,0.6136363636363636,0.8148148148148148,0.7708333333333334,0.9787234042553191,1.0,0.8505494505494505,0.514520525932312,0.22080102562904358,0.39328721165657043,0.235330268740654,0.13579073548316956,0.04352141544222832,0.45518078406651813,0.6094060838222504,0.28835128247737885,0.36743074655532837,0.12263910472393036,0.002220072317868471,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FederatedAveraging[batch_size=32,learning_rate=0.01,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAvg strategy.\n\nThis solver uses FLamby's implementation of FedAvg.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1602.05629",90,394.4339686420062,0.30655548086441636,0.2558660991489887,0.8388725001110595,0.30655548086441636,0.8575418994413407,0.6136363636363636,0.8333333333333334,0.75,0.9787234042553191,1.0,0.8512820512820513,0.5115814208984375,0.22077970206737518,0.3892914950847626,0.23560234904289246,0.13487713038921356,0.04306449741125107,0.4537986119588216,0.6072424948215485,0.2870299890637398,0.36662399768829346,0.12258081138134003,0.002056980272755027,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FederatedAveraging[batch_size=32,learning_rate=0.01,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAvg strategy.\n\nThis solver uses FLamby's implementation of FedAvg.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1602.05629",100,438.1074502200099,0.3057338749963997,0.2548523526638746,0.8357919501685102,0.3057338749963997,0.8603351955307262,0.6136363636363636,0.8333333333333334,0.75,0.9574468085106383,1.0,0.8523809523809524,0.508658766746521,0.22133155167102814,0.38600459694862366,0.23550193011760712,0.13465265929698944,0.04296461120247841,0.4529249568780263,0.6052351593971252,0.28565453737974167,0.36598529666662216,0.12266742065548897,0.0019358790013939142,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FederatedAveraging[batch_size=32,learning_rate=0.01,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAvg strategy.\n\nThis solver uses FLamby's implementation of FedAvg.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1602.05629",110,481.6153396590089,0.3050348142343056,0.2540187779814005,0.8378007093932843,0.3050348142343056,0.8631284916201117,0.6136363636363636,0.8425925925925926,0.75,0.9574468085106383,1.0,0.8527472527472527,0.5060436129570007,0.22195379436016083,0.3830718994140625,0.23541311919689178,0.13459502160549164,0.043035220354795456,0.4522700409094493,0.6035099625587463,0.28436558693647385,0.3654160276055336,0.1228114478290081,0.0018358195666223764,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FederatedAveraging[batch_size=32,learning_rate=0.01,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedAvg strategy.\n\nThis solver uses FLamby's implementation of FedAvg.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1602.05629",120,525.0755646940124,0.3044338480815188,0.25318309540549916,0.8362633693272784,0.3044338480815188,0.8659217877094972,0.6136363636363636,0.8518518518518519,0.75,0.9361702127659575,1.0,0.8534798534798534,0.5043480396270752,0.22189994156360626,0.3799076974391937,0.2359493374824524,0.13394977152347565,0.04304378479719162,0.45144521196683246,0.6023936867713928,0.2833690792322159,0.36473777890205383,0.12292332202196121,0.001734009594656527,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedProx[batch_size=32,learning_rate=0.01,mu=1.0,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedProx strategy.\n\nThis solver uses FLamby's implementation of FedProx\nby Li et al.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1812.06127",0,0.002286766000906937,0.4150216299523082,0.36538801590601605,0.6984627466471042,0.4150216299523082,0.5670391061452514,0.8181818181818182,0.7222222222222222,0.4166666666666667,1.0,0.6666666666666666,0.6923076923076923,0.8838081359863281,0.18267583847045898,0.5217576026916504,0.413883239030838,0.09549399465322495,0.09470928460359573,0.6022377709547678,0.8673385679721832,0.4092247486114502,0.4264897406101227,0.18259868025779724,0.002240271307528019,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedProx[batch_size=32,learning_rate=0.01,mu=1.0,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedProx strategy.\n\nThis solver uses FLamby's implementation of FedProx\nby Li et al.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1812.06127",10,44.44223063601021,0.3386181220929656,0.29454631234208745,0.7615816573023179,0.3386181220929656,0.6424581005586593,0.7727272727272727,0.6574074074074074,0.6458333333333334,0.851063829787234,1.0,0.7249084249084249,0.6281980872154236,0.21550026535987854,0.4265879690647125,0.25919002294540405,0.15755896270275116,0.0802425667643547,0.5167610247929891,0.6829835772514343,0.3091661334037781,0.37538786232471466,0.14073587954044342,0.00667425524443388,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedProx[batch_size=32,learning_rate=0.01,mu=1.0,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedProx strategy.\n\nThis solver uses FLamby's implementation of FedProx\nby Li et al.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1812.06127",20,88.89990894500806,0.33051465751810205,0.28543025627732277,0.7934778700805486,0.33051465751810205,0.7681564245810056,0.75,0.7037037037037037,0.6666666666666666,0.8723404255319149,1.0,0.7967032967032966,0.5985696911811829,0.2157982885837555,0.423948734998703,0.2518792152404785,0.15239937603473663,0.06998623162508011,0.4976128538449605,0.6654652655124664,0.30509722232818604,0.3742649704217911,0.1350259706377983,0.005621662363409996,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedProx[batch_size=32,learning_rate=0.01,mu=1.0,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedProx strategy.\n\nThis solver uses FLamby's implementation of FedProx\nby Li et al.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1812.06127",30,133.3211470450151,0.32484854643957484,0.2786101469149192,0.8166589441971787,0.32484854643957484,0.8072625698324022,0.75,0.7407407407407407,0.7083333333333334,0.8936170212765957,1.0,0.8234432234432234,0.5780209302902222,0.2154933512210846,0.4205085337162018,0.24773626029491425,0.1475602686405182,0.062341537326574326,0.48450515667597455,0.6530457139015198,0.3024500012397766,0.3731853738427162,0.13111762702465057,0.004787405952811241,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedProx[batch_size=32,learning_rate=0.01,mu=1.0,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedProx strategy.\n\nThis solver uses FLamby's implementation of FedProx\nby Li et al.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1812.06127",40,177.86923729601403,0.32076161784223384,0.27375435704986256,0.816891914809572,0.32076161784223384,0.8240223463687151,0.7045454545454546,0.75,0.7291666666666666,0.8936170212765957,1.0,0.8333333333333334,0.562727153301239,0.21637120842933655,0.4175047278404236,0.24406063556671143,0.144739031791687,0.05712338536977768,0.47620798150698346,0.6432149708271027,0.3000533729791641,0.37239376455545425,0.12848178297281265,0.004217834211885929,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedProx[batch_size=32,learning_rate=0.01,mu=1.0,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedProx strategy.\n\nThis solver uses FLamby's implementation of FedProx\nby Li et al.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1812.06127",50,222.33268759501516,0.3177151097657366,0.2701334860175848,0.8172714472538157,0.3177151097657366,0.8324022346368715,0.6590909090909091,0.7685185185185185,0.75,0.8936170212765957,1.0,0.841025641025641,0.5510648488998413,0.21758940815925598,0.4147011935710907,0.24107755720615387,0.14293472468852997,0.05343318358063698,0.4707378049691518,0.6353850960731506,0.29794466495513916,0.37174928188323975,0.1266767904162407,0.003797020297497511,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedProx[batch_size=32,learning_rate=0.01,mu=1.0,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedProx strategy.\n\nThis solver uses FLamby's implementation of FedProx\nby Li et al.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1812.06127",60,266.70478311800616,0.31535379390697926,0.2670725056280692,0.82282630576937,0.31535379390697926,0.835195530726257,0.6590909090909091,0.7777777777777778,0.75,0.9148936170212766,1.0,0.843956043956044,0.5428726077079773,0.21759137511253357,0.41119956970214844,0.23982174694538116,0.14053897559642792,0.05041075870394707,0.4663151800632477,0.6296659409999847,0.2964703291654587,0.3709375560283661,0.12532030791044235,0.0034134492743760347,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedProx[batch_size=32,learning_rate=0.01,mu=1.0,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedProx strategy.\n\nThis solver uses FLamby's implementation of FedProx\nby Li et al.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1812.06127",70,311.17782558300314,0.313489171068391,0.2647892963141203,0.8279156149366935,0.313489171068391,0.835195530726257,0.6590909090909091,0.7870370370370371,0.75,0.9361702127659575,1.0,0.8443223443223443,0.5358988046646118,0.21845881640911102,0.40833237767219543,0.2382357120513916,0.13942354917526245,0.04838651791214943,0.46347325046857196,0.6246667206287384,0.29487942159175873,0.37034154683351517,0.12443237379193306,0.003141713095828891,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedProx[batch_size=32,learning_rate=0.01,mu=1.0,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedProx strategy.\n\nThis solver uses FLamby's implementation of FedProx\nby Li et al.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1812.06127",80,355.68086934999883,0.31197778056634384,0.2629820617536704,0.8276738354395947,0.31197778056634384,0.835195530726257,0.6363636363636364,0.7870370370370371,0.75,0.9574468085106383,1.0,0.8443223443223443,0.5300524234771729,0.2195073366165161,0.40573614835739136,0.23679612576961517,0.1388399600982666,0.04696037620306015,0.4614814619223277,0.620421290397644,0.2933533191680908,0.3698408901691437,0.12384026870131493,0.0029294530395418406,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedProx[batch_size=32,learning_rate=0.01,mu=1.0,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedProx strategy.\n\nThis solver uses FLamby's implementation of FedProx\nby Li et al.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1812.06127",90,400.1169645629925,0.31071017435493153,0.2612625788897276,0.8292170453161379,0.31071017435493153,0.835195530726257,0.6363636363636364,0.7962962962962963,0.75,0.9574468085106383,1.0,0.845054945054945,0.526062548160553,0.2193754017353058,0.40247952938079834,0.23658694326877594,0.13743579387664795,0.04563525691628456,0.4594067831834157,0.6173831522464752,0.29227314889431,0.369145967066288,0.12333893775939941,0.002713056979700923,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedProx[batch_size=32,learning_rate=0.01,mu=1.0,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedProx strategy.\n\nThis solver uses FLamby's implementation of FedProx\nby Li et al.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1812.06127",100,444.54591557599633,0.3096475859767654,0.2599627412855625,0.8274379257530334,0.3096475859767654,0.8379888268156425,0.6136363636363636,0.8055555555555556,0.75,0.9574468085106383,1.0,0.8454212454212454,0.5221427083015442,0.22004099190235138,0.39989981055259705,0.23585522174835205,0.13700516521930695,0.04483254998922348,0.45810680588086444,0.6144798398017883,0.2910243198275566,0.36864810436964035,0.12306886538863182,0.002557580592110753,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedProx[batch_size=32,learning_rate=0.01,mu=1.0,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedProx strategy.\n\nThis solver uses FLamby's implementation of FedProx\nby Li et al.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1812.06127",110,488.9226790069988,0.30873964871797294,0.25889713192979497,0.8347811045929534,0.30873964871797294,0.8519553072625698,0.6136363636363636,0.8148148148148148,0.7708333333333334,0.9574468085106383,1.0,0.8483516483516483,0.5186074376106262,0.22086568176746368,0.39759063720703125,0.23510710895061493,0.13690099120140076,0.04431093484163284,0.457161804040273,0.6118980646133423,0.2897948622703552,0.3682265877723694,0.12292489036917686,0.0024316832423210144,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedProx[batch_size=32,learning_rate=0.01,mu=1.0,num_updates=100]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedProx strategy.\n\nThis solver uses FLamby's implementation of FedProx\nby Li et al.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1812.06127",120,533.350484585997,0.3079410328710866,0.25777636530498665,0.8343155552447225,0.3079410328710866,0.8491620111731844,0.6136363636363636,0.8148148148148148,0.7708333333333334,0.9574468085106383,1.0,0.8501831501831502,0.5163491368293762,0.22064077854156494,0.39467403292655945,0.2353370189666748,0.13594627380371094,0.04371095076203346,0.4559037486712138,0.6101467609405518,0.2889285832643509,0.3676151856780052,0.12275969609618187,0.0022922225762158632,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedYogi[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedYogi FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Yogi solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://papers.nips.cc/paper_files/paper/2018/hash/90365351ccc7437a1309dc64e4db32a3-Abstract.html  # noqa: E501\n- https://arxiv.org/abs/2003.00295",0,0.002476156001648633,0.4150216299523082,0.36538801590601605,0.6984627466471042,0.4150216299523082,0.5670391061452514,0.8181818181818182,0.7222222222222222,0.4166666666666667,1.0,0.6666666666666666,0.6923076923076923,0.8838081359863281,0.18267583847045898,0.5217576026916504,0.413883239030838,0.09549399465322495,0.09470928460359573,0.6022377709547678,0.8673385679721832,0.4092247486114502,0.4264897406101227,0.18259868025779724,0.002240271307528019,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedYogi[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedYogi FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Yogi solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://papers.nips.cc/paper_files/paper/2018/hash/90365351ccc7437a1309dc64e4db32a3-Abstract.html  # noqa: E501\n- https://arxiv.org/abs/2003.00295",10,43.380351563995646,0.35363287075112265,0.3013468148807685,0.7165629202667616,0.35363287075112265,0.6005586592178771,0.8409090909090909,0.7129629629629629,0.5208333333333334,0.9574468085106383,0.6666666666666666,0.7230769230769231,0.688846230506897,0.18759174644947052,0.42424166202545166,0.30810123682022095,0.12118567526340485,0.0781143382191658,0.5272784233093262,0.7231959402561188,0.33372533321380615,0.3818701207637787,0.15221364796161652,0.0035137590020895004,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedYogi[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedYogi FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Yogi solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://papers.nips.cc/paper_files/paper/2018/hash/90365351ccc7437a1309dc64e4db32a3-Abstract.html  # noqa: E501\n- https://arxiv.org/abs/2003.00295",20,86.72614700899067,0.3325264444057312,0.2873605899512768,0.7821530378337305,0.3325264444057312,0.7681564245810056,0.5681818181818182,0.7129629629629629,0.75,0.8936170212765957,1.0,0.7893772893772893,0.600342869758606,0.2267524003982544,0.41799917817115784,0.2474154829978943,0.1564081609249115,0.0752454474568367,0.5155483881632487,0.6547835767269135,0.2999153137207031,0.378425195813179,0.1416279524564743,0.004858239553868771,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedYogi[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedYogi FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Yogi solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://papers.nips.cc/paper_files/paper/2018/hash/90365351ccc7437a1309dc64e4db32a3-Abstract.html  # noqa: E501\n- https://arxiv.org/abs/2003.00295",30,130.07719879498836,0.32861928866865736,0.28871169686317444,0.7650683706193037,0.32861928866865736,0.8212290502793296,0.5227272727272727,0.6666666666666666,0.75,0.8297872340425532,1.0,0.7901098901098901,0.5815477967262268,0.24369044601917267,0.42622295022010803,0.23795954883098602,0.16626572608947754,0.07658371329307556,0.5116988718509674,0.6391287446022034,0.2936972379684448,0.38210853934288025,0.14046037942171097,0.004621958825737238,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedYogi[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedYogi FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Yogi solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://papers.nips.cc/paper_files/paper/2018/hash/90365351ccc7437a1309dc64e4db32a3-Abstract.html  # noqa: E501\n- https://arxiv.org/abs/2003.00295",40,173.3932686629778,0.3226311691105366,0.2810218781232834,0.777334702966909,0.3226311691105366,0.8072625698324022,0.5681818181818182,0.75,0.6875,0.851063829787234,1.0,0.8095238095238095,0.5687127709388733,0.23358087241649628,0.4115944504737854,0.24570748209953308,0.15572643280029297,0.0708092600107193,0.4934491813182831,0.6333761513233185,0.2907817140221596,0.3759297728538513,0.13859481364488602,0.003655381500720978,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedYogi[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedYogi FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Yogi solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://papers.nips.cc/paper_files/paper/2018/hash/90365351ccc7437a1309dc64e4db32a3-Abstract.html  # noqa: E501\n- https://arxiv.org/abs/2003.00295",50,216.78177187597612,0.31907275524766493,0.27505217492580414,0.788998525110049,0.31907275524766493,0.8016759776536313,0.6363636363636364,0.7777777777777778,0.6458333333333334,0.8723404255319149,1.0,0.8183150183150183,0.5627307295799255,0.22439280152320862,0.3982565402984619,0.2521686553955078,0.14639471471309662,0.06636960804462433,0.4812396764755249,0.6327438950538635,0.2887406498193741,0.3708196058869362,0.13786058872938156,0.0030321155209094286,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedYogi[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedYogi FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Yogi solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://papers.nips.cc/paper_files/paper/2018/hash/90365351ccc7437a1309dc64e4db32a3-Abstract.html  # noqa: E501\n- https://arxiv.org/abs/2003.00295",60,260.1163872489815,0.3165792467383047,0.27162278567751247,0.7971834547917197,0.3165792467383047,0.8156424581005587,0.6136363636363636,0.8148148148148148,0.6666666666666666,0.8723404255319149,1.0,0.8263736263736263,0.5552396774291992,0.2230866700410843,0.3915061354637146,0.25193163752555847,0.14351195096969604,0.0644606426358223,0.47523853182792664,0.6300722360610962,0.28556565940380096,0.3683469370007515,0.13746196031570435,0.0027901558205485344,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedYogi[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedYogi FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Yogi solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://papers.nips.cc/paper_files/paper/2018/hash/90365351ccc7437a1309dc64e4db32a3-Abstract.html  # noqa: E501\n- https://arxiv.org/abs/2003.00295",70,303.4270212429801,0.31454195190841955,0.2697431941827138,0.8029834237550966,0.31454195190841955,0.8296089385474861,0.6136363636363636,0.8148148148148148,0.6875,0.8723404255319149,1.0,0.8300366300366301,0.5463854074478149,0.2261204719543457,0.3893211781978607,0.2491675317287445,0.14389726519584656,0.06356731057167053,0.4722515940666199,0.6254770457744598,0.28230245411396027,0.367611289024353,0.1369205340743065,0.002688794396817684,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedYogi[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedYogi FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Yogi solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://papers.nips.cc/paper_files/paper/2018/hash/90365351ccc7437a1309dc64e4db32a3-Abstract.html  # noqa: E501\n- https://arxiv.org/abs/2003.00295",80,346.7292163969796,0.31302405623460394,0.2686132366458575,0.8134071525375658,0.31302405623460394,0.840782122905028,0.6136363636363636,0.8240740740740741,0.7083333333333334,0.8936170212765957,1.0,0.836996336996337,0.5394128561019897,0.2291382998228073,0.3886347711086273,0.24769718945026398,0.14416778087615967,0.06262852251529694,0.47024386127789813,0.6214177012443542,0.2800879031419754,0.36752063035964966,0.1362920179963112,0.0025822233874350786,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedYogi[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedYogi FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Yogi solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://papers.nips.cc/paper_files/paper/2018/hash/90365351ccc7437a1309dc64e4db32a3-Abstract.html  # noqa: E501\n- https://arxiv.org/abs/2003.00295",90,390.03326716097945,0.31182111202118296,0.26752525630096596,0.8120935823226921,0.31182111202118296,0.8463687150837989,0.5909090909090909,0.8333333333333334,0.7083333333333334,0.8936170212765957,1.0,0.8377289377289378,0.5345830321311951,0.23052871227264404,0.38746771216392517,0.24778048694133759,0.1433601975440979,0.0614313967525959,0.46821239590644836,0.6185759007930756,0.2787366583943367,0.3672425001859665,0.1357039362192154,0.002455280628055334,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedYogi[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedYogi FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Yogi solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://papers.nips.cc/paper_files/paper/2018/hash/90365351ccc7437a1309dc64e4db32a3-Abstract.html  # noqa: E501\n- https://arxiv.org/abs/2003.00295",100,433.4657156119829,0.3108012229721579,0.26637463085353374,0.8139524487648918,0.3108012229721579,0.8463687150837989,0.6136363636363636,0.8425925925925926,0.6875,0.8936170212765957,1.0,0.836996336996337,0.5309252142906189,0.230914905667305,0.3857560455799103,0.2482655793428421,0.1421171873807907,0.06026885285973549,0.4662122031052907,0.6165683269500732,0.27774161845445633,0.36666323244571686,0.13528414070606232,0.002337816171348095,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedYogi[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedYogi FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Yogi solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://papers.nips.cc/paper_files/paper/2018/hash/90365351ccc7437a1309dc64e4db32a3-Abstract.html  # noqa: E501\n- https://arxiv.org/abs/2003.00295",110,476.7756595259816,0.3099118531164196,0.2653048038482666,0.8147941996066427,0.3099118531164196,0.8463687150837989,0.5909090909090909,0.8703703703703703,0.6875,0.8936170212765957,1.0,0.8388278388278388,0.5276749134063721,0.23120005428791046,0.38406798243522644,0.24846839904785156,0.14108707010746002,0.05933040380477905,0.46449119846026105,0.6148687601089478,0.27684272080659866,0.3659989759325981,0.13502750545740128,0.0022419579327106476,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","FedYogi[batch_size=32,beta1=0.9,beta2=0.999,learning_rate=0.001,num_updates=100,server_learning_rate=0.001,tau=1e-08]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the FedYogi FL strategy.\n\nThis solver uses FLamby's implementation of the federation\nof the Yogi solver as described in Reddi et al. 2020.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://papers.nips.cc/paper_files/paper/2018/hash/90365351ccc7437a1309dc64e4db32a3-Abstract.html  # noqa: E501\n- https://arxiv.org/abs/2003.00295",120,520.037327328977,0.3091315476016866,0.26440132160981494,0.8157252983031045,0.3091315476016866,0.8519553072625698,0.5909090909090909,0.8703703703703703,0.6875,0.8936170212765957,1.0,0.8395604395604396,0.5246274471282959,0.2317013293504715,0.38267940282821655,0.24842502176761627,0.14039196074008942,0.058582767844200134,0.46310404936472577,0.6132341921329498,0.27602308988571167,0.3654154762625694,0.13485228270292282,0.0021601952612400055,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Scaffold[batch_size=32,learning_rate=0.01,num_updates=100,server_learning_rate=1.0]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Scaffold FL strategy.\n\nBy adding auxiliary variables or control variates (c) in the\nclients and server the authors of the related paper prove better\nbounds on the convergence assuming certain\nhypothesis. FLamby implements a faster version of Scaffold.\nThe details can be found in FLamby.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1910.06378",0,0.0025061720007215627,0.4150216299523082,0.36538801590601605,0.6984627466471042,0.4150216299523082,0.5670391061452514,0.8181818181818182,0.7222222222222222,0.4166666666666667,1.0,0.6666666666666666,0.6923076923076923,0.8838081359863281,0.18267583847045898,0.5217576026916504,0.413883239030838,0.09549399465322495,0.09470928460359573,0.6022377709547678,0.8673385679721832,0.4092247486114502,0.4264897406101227,0.18259868025779724,0.002240271307528019,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Scaffold[batch_size=32,learning_rate=0.01,num_updates=100,server_learning_rate=1.0]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Scaffold FL strategy.\n\nBy adding auxiliary variables or control variates (c) in the\nclients and server the authors of the related paper prove better\nbounds on the convergence assuming certain\nhypothesis. FLamby implements a faster version of Scaffold.\nThe details can be found in FLamby.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1910.06378",10,44.026358754988905,0.3332613416067842,0.28845375031232834,0.7823272186249621,0.3332613416067842,0.7178770949720671,0.7727272727272727,0.6851851851851852,0.6458333333333334,0.8723404255319149,1.0,0.7721611721611722,0.6088153719902039,0.21566958725452423,0.424667626619339,0.25491663813591003,0.15345437824726105,0.07319889962673187,0.5038501818974813,0.6714669466018677,0.3066262900829315,0.37489116191864014,0.1367751881480217,0.0059582809917628765,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Scaffold[batch_size=32,learning_rate=0.01,num_updates=100,server_learning_rate=1.0]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Scaffold FL strategy.\n\nBy adding auxiliary variables or control variates (c) in the\nclients and server the authors of the related paper prove better\nbounds on the convergence assuming certain\nhypothesis. FLamby implements a faster version of Scaffold.\nThe details can be found in FLamby.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1910.06378",20,88.29036682898368,0.32368713808763355,0.2774364482611418,0.813480637729568,0.32368713808763355,0.8100558659217877,0.7045454545454546,0.7222222222222222,0.7291666666666666,0.9148936170212766,1.0,0.8267399267399267,0.5736391544342041,0.21759900450706482,0.4197165071964264,0.24664948880672455,0.14632271230220795,0.060691822320222855,0.4829499026139577,0.6491308212280273,0.3014945387840271,0.3738671988248825,0.1300160549581051,0.004664312116801739,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Scaffold[batch_size=32,learning_rate=0.01,num_updates=100,server_learning_rate=1.0]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Scaffold FL strategy.\n\nBy adding auxiliary variables or control variates (c) in the\nclients and server the authors of the related paper prove better\nbounds on the convergence assuming certain\nhypothesis. FLamby implements a faster version of Scaffold.\nThe details can be found in FLamby.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1910.06378",30,132.42210469998463,0.31800160370767117,0.270439734061559,0.8249757570154573,0.31800160370767117,0.8268156424581006,0.6590909090909091,0.7777777777777778,0.75,0.9361702127659575,1.0,0.8413919413919414,0.5538169741630554,0.21841688454151154,0.41398653388023376,0.24325573444366455,0.14021432399749756,0.05294795334339142,0.4717784523963928,0.6350691616535187,0.2983676642179489,0.37291035056114197,0.1260991245508194,0.0037848688662052155,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Scaffold[batch_size=32,learning_rate=0.01,num_updates=100,server_learning_rate=1.0]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Scaffold FL strategy.\n\nBy adding auxiliary variables or control variates (c) in the\nclients and server the authors of the related paper prove better\nbounds on the convergence assuming certain\nhypothesis. FLamby implements a faster version of Scaffold.\nThe details can be found in FLamby.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1910.06378",40,176.52369462198476,0.3143438587172164,0.26627798192203045,0.8187966474843926,0.3143438587172164,0.835195530726257,0.6136363636363636,0.7777777777777778,0.75,0.9361702127659575,1.0,0.8446886446886447,0.5399762392044067,0.2212914675474167,0.409969687461853,0.2396935671567917,0.1378832459449768,0.04885368421673775,0.46670936544736225,0.624409407377243,0.295259028673172,0.37244391441345215,0.12394462898373604,0.0032968074083328247,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Scaffold[batch_size=32,learning_rate=0.01,num_updates=100,server_learning_rate=1.0]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Scaffold FL strategy.\n\nBy adding auxiliary variables or control variates (c) in the\nclients and server the authors of the related paper prove better\nbounds on the convergence assuming certain\nhypothesis. FLamby implements a faster version of Scaffold.\nThe details can be found in FLamby.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1910.06378",50,220.64349476198186,0.31180310898667407,0.2633961532264948,0.8274320559424962,0.31180310898667407,0.835195530726257,0.6136363636363636,0.7870370370370371,0.75,0.9787234042553191,1.0,0.8472527472527472,0.5305804014205933,0.22344855964183807,0.40613946318626404,0.2375633716583252,0.1362607777118683,0.04638434574007988,0.4637795587380727,0.6167424321174622,0.2927316874265671,0.3719399571418762,0.12267967686057091,0.0029453416354954243,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Scaffold[batch_size=32,learning_rate=0.01,num_updates=100,server_learning_rate=1.0]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Scaffold FL strategy.\n\nBy adding auxiliary variables or control variates (c) in the\nclients and server the authors of the related paper prove better\nbounds on the convergence assuming certain\nhypothesis. FLamby implements a faster version of Scaffold.\nThe details can be found in FLamby.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1910.06378",60,264.740294954976,0.3098852545178185,0.26095156619946164,0.8356136546734431,0.3098852545178185,0.8379888268156425,0.6136363636363636,0.8333333333333334,0.75,0.9787234042553191,1.0,0.8483516483516483,0.525286853313446,0.22336895763874054,0.40126195549964905,0.2377028614282608,0.13359896838665009,0.04448980093002319,0.46109575033187866,0.6117400527000427,0.29102396219968796,0.371027909219265,0.12180061265826225,0.0026232399977743626,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Scaffold[batch_size=32,learning_rate=0.01,num_updates=100,server_learning_rate=1.0]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Scaffold FL strategy.\n\nBy adding auxiliary variables or control variates (c) in the\nclients and server the authors of the related paper prove better\nbounds on the convergence assuming certain\nhypothesis. FLamby implements a faster version of Scaffold.\nThe details can be found in FLamby.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1910.06378",70,308.9391857549781,0.3084237748601784,0.2594700437039137,0.8449597229276001,0.3084237748601784,0.8519553072625698,0.6136363636363636,0.8333333333333334,0.7708333333333334,1.0,1.0,0.8527472527472527,0.5196806192398071,0.2252221405506134,0.3982257843017578,0.23640485107898712,0.1334616243839264,0.043825242668390274,0.4600571095943451,0.6071023046970367,0.28892338275909424,0.3705511912703514,0.121466264128685,0.0024423967115581036,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Scaffold[batch_size=32,learning_rate=0.01,num_updates=100,server_learning_rate=1.0]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Scaffold FL strategy.\n\nBy adding auxiliary variables or control variates (c) in the\nclients and server the authors of the related paper prove better\nbounds on the convergence assuming certain\nhypothesis. FLamby implements a faster version of Scaffold.\nThe details can be found in FLamby.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1910.06378",80,353.0702245439825,0.307245767261419,0.25825855508446693,0.8474340315006051,0.307245767261419,0.8575418994413407,0.6136363636363636,0.8425925925925926,0.7708333333333334,1.0,1.0,0.8538461538461538,0.5155172348022461,0.22640016674995422,0.3951425552368164,0.23578554391860962,0.1332254558801651,0.04348037391901016,0.45915629466374713,0.6035870015621185,0.28714147210121155,0.36999206990003586,0.12130998075008392,0.002287784591317177,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Scaffold[batch_size=32,learning_rate=0.01,num_updates=100,server_learning_rate=1.0]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Scaffold FL strategy.\n\nBy adding auxiliary variables or control variates (c) in the\nclients and server the authors of the related paper prove better\nbounds on the convergence assuming certain\nhypothesis. FLamby implements a faster version of Scaffold.\nThe details can be found in FLamby.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1910.06378",90,397.1919857479843,0.30622975817985004,0.2569306356211503,0.850373889421841,0.30622975817985004,0.8659217877094972,0.6136363636363636,0.8518518518518519,0.7708333333333334,1.0,1.0,0.8582417582417582,0.5136691927909851,0.2256324589252472,0.3909381628036499,0.23682698607444763,0.13153891265392303,0.04297810047864914,0.45766813556353253,0.601459801197052,0.28596220910549164,0.3690447062253952,0.12113099545240402,0.0021127015352249146,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Scaffold[batch_size=32,learning_rate=0.01,num_updates=100,server_learning_rate=1.0]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Scaffold FL strategy.\n\nBy adding auxiliary variables or control variates (c) in the\nclients and server the authors of the related paper prove better\nbounds on the convergence assuming certain\nhypothesis. FLamby implements a faster version of Scaffold.\nThe details can be found in FLamby.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1910.06378",100,441.29387241398217,0.30541396252293557,0.25622287516792613,0.850839438770072,0.30541396252293557,0.8687150837988827,0.6136363636363636,0.8518518518518519,0.7708333333333334,1.0,1.0,0.8582417582417582,0.5104328393936157,0.22689072787761688,0.38857215642929077,0.23625315725803375,0.13199208676815033,0.04319628328084946,0.45727376143137616,0.5989862084388733,0.2843824625015259,0.3685746043920517,0.1212470717728138,0.002019666600972414,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Scaffold[batch_size=32,learning_rate=0.01,num_updates=100,server_learning_rate=1.0]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Scaffold FL strategy.\n\nBy adding auxiliary variables or control variates (c) in the\nclients and server the authors of the related paper prove better\nbounds on the convergence assuming certain\nhypothesis. FLamby implements a faster version of Scaffold.\nThe details can be found in FLamby.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1910.06378",110,485.3604673509799,0.3047109981093349,0.25556482126315433,0.8468277901310609,0.3047109981093349,0.8659217877094972,0.6136363636363636,0.8518518518518519,0.7708333333333334,0.9787234042553191,1.0,0.8571428571428571,0.5080088376998901,0.22759580612182617,0.38608747720718384,0.23616208136081696,0.13210265338420868,0.043432071805000305,0.45678643385569256,0.5970856547355652,0.283053420484066,0.3680334910750389,0.12137709558010101,0.0019298929255455732,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]
["FLamby[seed=42]","Scaffold[batch_size=32,learning_rate=0.01,num_updates=100,server_learning_rate=1.0]","Fed-TCGA-BRCA[seed=42,test=test,train=fl]",0,"Iteration","Compute per-client and global metrics and losses.\n\n    This objective computes several metrics of interests based on FLamby losses\n    and Flamby metrics per dataset and globally.\n    This way we can compare per-dataset metric vs averaged metrics across\n    datasets and global metric when datasets are pooled.\n    The objective_value is the average train or test loss across datasets\n    this depends on wether we use the validation datasets or the test\n    ones.\n\n\n    Parameters\n    ----------\n    BaseObjective : BaseObjective\n        The base objective in BenchOpt\n\n    ","Implement the Scaffold FL strategy.\n\nBy adding auxiliary variables or control variates (c) in the\nclients and server the authors of the related paper prove better\nbounds on the convergence assuming certain\nhypothesis. FLamby implements a faster version of Scaffold.\nThe details can be found in FLamby.\n\nParameters\n----------\nFLambySolver : FlambySolver\n    We define a common interface for all strategies implemented\n    in FLamby.\n\nReferences\n----------\n- https://arxiv.org/abs/1910.06378",120,529.5111472679855,0.30406178124637034,0.25467736336092156,0.8448987777853819,0.30406178124637034,0.8659217877094972,0.6136363636363636,0.8611111111111112,0.75,0.9787234042553191,1.0,0.8556776556776556,0.5073701739311218,0.22661955654621124,0.38255181908607483,0.2374531775712967,0.13072893023490906,0.043340522795915604,0.45568567514419556,0.596110999584198,0.2822217643260956,0.367145374417305,0.12139338999986649,0.001813484006561339,null,"Linux","x86_64","5.10.214-202.855.amzn2.x86_64","#1 SMP Tue Apr 9 06:57:12 UTC 2024",16,"Intel(R) Xeon(R) Platinum 8488C",62,null,["1.26.4",""],"1.13.1",null]