{"id":"W4410115263","doi":"10.1109/lcomm.2025.3567387","title":"Over-the-Air Fair Federated Learning via Multi-Objective Optimization","year":2025,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Computer science; Computer network; Distributed computing","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":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0004094601,0.0001651247,0.0001451232,0.0002416818,0.00118481,0.0002599591,0.03133029,0.00009772707,0.000003597825],"category_scores_gemma":[0.003667016,0.0001536629,0.000058582,0.001376058,0.0003167838,0.0006479277,0.03354754,0.0007112579,0.00002644596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002086745,"about_ca_system_score_gemma":0.00006401392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001545248,"about_ca_topic_score_gemma":0.00006080038,"domain_scores_codex":[0.998486,0.0004269212,0.000283659,0.000370167,0.0001659305,0.0002672739],"domain_scores_gemma":[0.9871371,0.0006094637,0.000158951,0.01194521,0.000123549,0.00002570502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002119703,0.000658469,0.005549017,0.0000534082,0.0005472401,0.00001192486,0.001474556,0.2866072,0.04842804,0.007155085,0.5621647,0.08732919],"study_design_scores_gemma":[0.0002669854,0.00001033166,0.001686206,0.00003992096,0.00001018244,0.000003054713,0.00005310523,0.9902633,0.002072851,0.001623257,0.003806921,0.0001639181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002492548,0.0001591831,0.8355973,0.1597574,0.0002489186,0.0002554361,0.000002959587,0.001030666,0.0004555629],"genre_scores_gemma":[0.4995052,0.0001258317,0.4962091,0.003934774,0.000009295853,0.0001120383,0.00002984263,0.00001210512,0.00006175457],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7036561,"threshold_uncertainty_score":0.974269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02868399823886364,"score_gpt":0.2932410083323019,"score_spread":0.2645570100934382,"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."}}