{"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":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.002738945,0.0001457355,0.0002694928,0.00005923475,0.000825491,0.0005327422,0.02326255,0.0000807442,0.00000628284],"category_scores_gemma":[0.0004257884,0.0001142832,0.0002828113,0.0008871158,0.00004203241,0.0005139332,0.03181002,0.00117637,5.667109e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004198769,"about_ca_system_score_gemma":0.0002264255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001426905,"about_ca_topic_score_gemma":0.000003735302,"domain_scores_codex":[0.9980705,0.0004489072,0.0004589143,0.0003105169,0.0003813293,0.0003297786],"domain_scores_gemma":[0.9977965,0.0002262852,0.0005754541,0.001194844,0.0001635806,0.00004334914],"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":[0.00002355621,0.0001003123,0.001581304,0.000006830133,0.00004960571,0.000007464698,0.00009692814,0.7459243,0.000162415,0.0001352462,0.2432308,0.008681185],"study_design_scores_gemma":[0.001017943,0.0003211005,0.0004537332,0.00004073163,0.000008442057,0.0001244969,0.00003187385,0.9827436,0.0003065639,0.009461822,0.005350378,0.000139277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02047664,0.0001034567,0.9583092,0.01865001,0.001947476,0.0004177941,0.000001667893,0.00006518422,0.00002852829],"genre_scores_gemma":[0.4833469,0.00001682207,0.5139486,0.002424882,0.0001650943,0.00003572514,0.000002380042,0.00001632063,0.0000432896],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4628703,"threshold_uncertainty_score":0.982022,"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."}}