{"id":"W7123351483","doi":"10.1109/dasc68382.2025.00020","title":"FedCIM: Handling Data Heterogeneity in Federated Learning to Improve Fairness and Robustness","year":2025,"lang":"","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Cégep de Chicoutimi; Université du Québec à Chicoutimi","funders":"","keywords":"Federated learning; Robustness (evolution); Cluster analysis; Exploit; Key (lock); Information privacy; Task (project management); Data modeling","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":[0.01340123,0.001048563,0.0020483,0.001098839,0.001942871,0.002718242,0.00408669,0.001834423,0.001574092],"category_scores_gemma":[0.03194879,0.0005186047,0.00113639,0.001441372,0.001988615,0.004363219,0.005774122,0.002897836,0.0004792901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002003519,"about_ca_system_score_gemma":0.003998905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003975646,"about_ca_topic_score_gemma":0.003109907,"domain_scores_codex":[0.9935036,0.002528036,0.0003788997,0.001548151,0.001403689,0.0006376685],"domain_scores_gemma":[0.9851445,0.006248438,0.0008747765,0.005247016,0.001907281,0.0005779036],"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":[0.0006472638,0.0003849789,0.004517299,0.0001195835,0.0001748643,0.0002181804,0.0003928973,0.7620878,0.005010284,0.03040649,0.003804072,0.1922362],"study_design_scores_gemma":[0.00002605718,0.00005264221,0.0001762583,0.00001041934,0.00001072061,0.00004754041,0.00002996578,0.9748179,0.002506343,0.02157718,0.0007321253,0.00001294362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01861791,0.0001298608,0.9786069,0.0002152817,0.00004410286,0.00008239398,0.00006090463,0.001487771,0.0007548404],"genre_scores_gemma":[0.7246038,0.00009001666,0.2726816,0.0004267721,0.0000700425,0.0002465675,0.00025296,0.0001953357,0.00143289],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01340123,"threshold_uncertainty_score":0.07087332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04674639911020517,"score_gpt":0.3141694599659617,"score_spread":0.2674230608557566,"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."}}