{"id":"W4396949269","doi":"10.1109/isqed60706.2024.10528721","title":"Learning Client Selection Strategy for Federated Learning across Heterogeneous Mobile Devices","year":2024,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Selection (genetic algorithm); Mobile device; Mobile computing; Federated learning; Human–computer interaction; Distributed computing; World Wide Web; Artificial intelligence; Computer network","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":[0.002459079,0.0007630796,0.001432088,0.0005818889,0.0009869208,0.001100526,0.00277345,0.001358076,0.002292878],"category_scores_gemma":[0.005667013,0.0003771758,0.0004554644,0.0006641469,0.000992217,0.00186982,0.00206471,0.001252474,0.000626273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001165826,"about_ca_system_score_gemma":0.001691099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00365159,"about_ca_topic_score_gemma":0.003364153,"domain_scores_codex":[0.9986089,0.0004458005,0.00008101924,0.0003186664,0.0002569108,0.0002887284],"domain_scores_gemma":[0.9975205,0.001013186,0.0001946313,0.0004466336,0.0005717861,0.0002532702],"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":[0.0007536038,0.0004859441,0.005005314,0.00007266205,0.00008189803,0.0004816179,0.0002405492,0.7251593,0.006789385,0.01717301,0.004949285,0.2388074],"study_design_scores_gemma":[0.00001352453,0.00002642434,0.00007528635,0.000001735278,0.000003413182,0.00002534635,0.0000120672,0.9968526,0.0008203951,0.002032488,0.0001334138,0.000003342196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0609946,0.0001363241,0.9350771,0.0003602913,0.00003823841,0.0001001232,0.00003521485,0.001560646,0.001697457],"genre_scores_gemma":[0.9261178,0.00004980996,0.07115339,0.0001874391,0.000027795,0.0001096025,0.00006858903,0.00005270009,0.002232852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00365159,"threshold_uncertainty_score":0.01300502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03606837618804754,"score_gpt":0.3304233048555244,"score_spread":0.2943549286674769,"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."}}