{"id":"W4226358703","doi":"10.1109/ojcs.2022.3163620","title":"Fuzzy Logic Based Client Selection for Federated Learning in Vehicular Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Open Journal of the Computer Society","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Toyota Motor Corporation; Canadian Institute for Advanced Research","keywords":"Computer science; Selection (genetic algorithm); Fuzzy logic; Federated learning; Computer network; Artificial intelligence; Computer security","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001967701,0.0005432069,0.001254734,0.0008405352,0.001128885,0.001486074,0.002661139,0.001140587,0.001405105],"category_scores_gemma":[0.00347061,0.0002499316,0.0004803267,0.001014646,0.000615062,0.001770292,0.001439013,0.0007442289,0.0003228308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001749451,"about_ca_system_score_gemma":0.001579603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003943668,"about_ca_topic_score_gemma":0.004252208,"domain_scores_codex":[0.9980454,0.000536293,0.0001330285,0.0003734419,0.0005413862,0.0003703911],"domain_scores_gemma":[0.9981864,0.0006410931,0.0001780736,0.0003302698,0.0004692639,0.0001947442],"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":[0.001772583,0.0005268958,0.005497247,0.0001762264,0.0001575087,0.0009531029,0.0004509231,0.5442153,0.02021729,0.0356085,0.005822721,0.3846017],"study_design_scores_gemma":[0.00002017634,0.00005035043,0.0002011453,0.000005971109,0.0000138531,0.0001170762,0.00004530015,0.9878474,0.004059092,0.007136733,0.0004898068,0.00001297183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1177881,0.0005326958,0.8775057,0.0003672978,0.00007374159,0.0001675865,0.00008307993,0.001185014,0.002296767],"genre_scores_gemma":[0.9709192,0.00008704772,0.02742149,0.00007790252,0.00001429178,0.00004130031,0.00005229629,0.00001062437,0.001375789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003943668,"threshold_uncertainty_score":0.01269323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03719450080659792,"score_gpt":0.2826073137745809,"score_spread":0.245412812967983,"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."}}