{"id":"W4411019390","doi":"10.1109/tai.2025.3576201","title":"pFedBL: Federated Bayesian Learning With Personalized Prior","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Bayesian probability; Federated learning; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.007520027,0.001543831,0.002645141,0.001238547,0.001156024,0.003158282,0.006209488,0.003314837,0.006441886],"category_scores_gemma":[0.01969296,0.001275336,0.001271957,0.001751271,0.00179567,0.006787232,0.006411362,0.004761943,0.002364703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003584565,"about_ca_system_score_gemma":0.004913542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01255166,"about_ca_topic_score_gemma":0.01602769,"domain_scores_codex":[0.9951303,0.001549345,0.0002480674,0.001092779,0.001446352,0.0005332436],"domain_scores_gemma":[0.9939396,0.002810903,0.0003870463,0.001190288,0.001274122,0.0003979851],"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":[0.0004227538,0.0004336245,0.002150992,0.0002094885,0.000107921,0.0002217934,0.0002536322,0.6903482,0.001784793,0.07121412,0.01292721,0.2199254],"study_design_scores_gemma":[0.00002399493,0.00002087539,0.00008433966,0.00001497126,0.0000065012,0.00003634976,0.00001513422,0.9667414,0.0005266345,0.03138541,0.001132184,0.00001211493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003377808,0.0002404111,0.9929128,0.0004137897,0.00002387369,0.00007668968,0.0001800245,0.001649747,0.00112484],"genre_scores_gemma":[0.4080979,0.0007476879,0.5797957,0.001598861,0.0001473983,0.0007231645,0.001587863,0.0005603631,0.006741126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01255166,"threshold_uncertainty_score":0.03977025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03914861593838184,"score_gpt":0.3001625706292194,"score_spread":0.2610139546908375,"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."}}