{"id":"W4323646022","doi":"10.1109/fnwf55208.2022.00024","title":"A Shapley value-enhanced evaluation technique for effective aggregation in Federated Learning","year":2022,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Federated learning; Aggregate (composite); Shapley value; Divergence (linguistics); Independent and identically distributed random variables; Machine learning; Data sharing; Data modeling; Data mining; Artificial intelligence; Internet of Things; Computer security; Database; Game theory","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008180772,0.001574219,0.002021661,0.001575952,0.001172913,0.002216579,0.002334808,0.001229327,0.002852245],"category_scores_gemma":[0.0164665,0.0003636225,0.0009802384,0.002074734,0.001397585,0.004678193,0.00212988,0.002241148,0.000324123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002890113,"about_ca_system_score_gemma":0.0020791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00187068,"about_ca_topic_score_gemma":0.001743238,"domain_scores_codex":[0.9947659,0.002720965,0.0002774008,0.0006195927,0.001317625,0.0002985972],"domain_scores_gemma":[0.9938497,0.003239235,0.0004376075,0.0007239575,0.001412833,0.0003366362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002682555,0.0002277668,0.001820862,0.000209407,0.0002060755,0.0001639462,0.0003119661,0.5939358,0.002991962,0.195175,0.004918383,0.1997705],"study_design_scores_gemma":[0.00001407161,0.00008230351,0.0001155179,0.00001428972,0.00001587154,0.00004031336,0.00002351105,0.9480796,0.0007724901,0.0501724,0.0006556038,0.0000139389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01084135,0.0001846418,0.9861969,0.0001628999,0.00005071478,0.00009868816,0.00004498315,0.0001605732,0.002259238],"genre_scores_gemma":[0.7199209,0.0003179129,0.27678,0.0001964118,0.0001075109,0.0002292849,0.0001910904,0.00008409874,0.002172749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008180772,"threshold_uncertainty_score":0.04326457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02789451643622128,"score_gpt":0.3112635399889312,"score_spread":0.2833690235527099,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}