{"id":"W4413145876","doi":"10.1109/cvpr52734.2025.01439","title":"FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Layer (electronics); Artificial intelligence; Human–computer interaction; Nanotechnology; Materials science","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":[0.002716963,0.001151268,0.001337813,0.0006473406,0.0009613182,0.001424586,0.003133351,0.001379786,0.001548481],"category_scores_gemma":[0.006794557,0.0005109814,0.000819784,0.0007791666,0.0008981088,0.004874164,0.003072482,0.00229362,0.0004721259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001446539,"about_ca_system_score_gemma":0.002154735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004731788,"about_ca_topic_score_gemma":0.009333634,"domain_scores_codex":[0.9987183,0.0002961356,0.00009972866,0.0003416427,0.0003318837,0.0002122335],"domain_scores_gemma":[0.9976882,0.0005990753,0.0001997264,0.001020645,0.0003416793,0.0001506928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000479949,0.0004736045,0.008352238,0.0001525567,0.0002256744,0.0002322588,0.0006112076,0.333557,0.01125492,0.01290619,0.01141248,0.6203419],"study_design_scores_gemma":[0.00002009972,0.00005919073,0.0003040441,0.000009682886,0.00002720166,0.00007318697,0.00005889797,0.9854869,0.004323727,0.008198773,0.001425888,0.00001236772],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06012359,0.0006870347,0.9318085,0.0004676839,0.0000870385,0.0001160601,0.0001426353,0.005070081,0.001497302],"genre_scores_gemma":[0.7209309,0.0001962566,0.2742324,0.0005681009,0.0000684937,0.0001395849,0.0004654501,0.0002729968,0.003125797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004731788,"threshold_uncertainty_score":0.01436883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0283614440162595,"score_gpt":0.3022973286482312,"score_spread":0.2739358846319717,"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."}}