{"id":"W4407217313","doi":"10.1016/j.neucom.2025.129528","title":"Multi-level analyzation of imbalance to resolve non-IID-ness in federated learning","year":2025,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Institute for Information and Communications Technology Promotion; Seoul National University; Ministry of Health and Welfare; National Research Foundation of Korea; Ministry of Food and Drug Safety; Ministry of Trade, Industry and Energy","keywords":"Computer science; Federated learning; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000444321,0.0001347012,0.0002269302,0.0004519023,0.0001290411,0.0001150534,0.0007436202,0.00006272931,8.17799e-7],"category_scores_gemma":[0.0003531488,0.0001516002,0.0000322207,0.0019895,0.00002011924,0.0002519904,0.0004278041,0.0002515661,0.000006899945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006385408,"about_ca_system_score_gemma":0.00008205429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005144946,"about_ca_topic_score_gemma":0.00002127657,"domain_scores_codex":[0.9984443,0.000128682,0.0004737186,0.0005132358,0.0001804865,0.0002595361],"domain_scores_gemma":[0.9990079,0.000161551,0.0001996873,0.0003993404,0.0001917756,0.00003976753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003883546,0.0003600509,0.1261788,0.0002238499,0.00002308875,0.00002820671,0.001828812,0.05037718,0.5295727,0.007621638,0.001218389,0.2825284],"study_design_scores_gemma":[0.0002805953,0.00002795667,0.2845511,0.0001565497,0.000001380944,0.000001230025,0.00002179595,0.6646808,0.04967495,0.00005679644,0.0004318123,0.0001149707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05658462,0.00000941027,0.9418342,0.0004231784,0.0001283657,0.0002849149,0.000001617306,0.0002445123,0.000489218],"genre_scores_gemma":[0.748415,0.000002476833,0.2511128,0.0003202386,0.00001135682,0.00001407875,0.000006054481,0.000007120578,0.0001109122],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6918304,"threshold_uncertainty_score":0.6182078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02804857037053525,"score_gpt":0.3093686405149193,"score_spread":0.281320070144384,"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."}}