{"id":"W4409014267","doi":"10.1109/access.2025.3556587","title":"Resource-Efficient Personalization in Federated Learning With Closed-Form Classifiers","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute","funders":"","keywords":"Personalization; Computer science; Resource (disambiguation); Resource management (computing); Information retrieval; Artificial intelligence; Machine learning; World Wide Web; Distributed computing; Computer network","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":[0.005413789,0.001013008,0.001995954,0.0007598049,0.0008503623,0.002439306,0.002806574,0.001672088,0.002792086],"category_scores_gemma":[0.0162719,0.000771738,0.0010139,0.00116411,0.001204018,0.004472098,0.002858575,0.002485406,0.001640822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001590123,"about_ca_system_score_gemma":0.002391572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004242451,"about_ca_topic_score_gemma":0.004751917,"domain_scores_codex":[0.9956234,0.001361226,0.0002824859,0.001239653,0.00100206,0.0004911985],"domain_scores_gemma":[0.9939666,0.0021516,0.0003758208,0.002250718,0.001063834,0.0001914678],"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":[0.000373119,0.0003528339,0.002385324,0.00007134237,0.00007294483,0.0002023841,0.0002529923,0.6581752,0.004602535,0.0253474,0.004989424,0.3031745],"study_design_scores_gemma":[0.00001190375,0.00001912903,0.00007881374,0.000004029689,0.000004933936,0.00002378409,0.00001593416,0.9872139,0.001210571,0.01092641,0.0004852267,0.000005364432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01656724,0.00009414539,0.9796382,0.0001653061,0.0000188599,0.00004811784,0.00005029992,0.002405362,0.001012401],"genre_scores_gemma":[0.6000941,0.0001408014,0.3940973,0.0002845672,0.00007142717,0.0002236539,0.0003632823,0.0003934843,0.004331376],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005413789,"threshold_uncertainty_score":0.02863115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02962672376843315,"score_gpt":0.3000816292745683,"score_spread":0.2704549055061352,"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."}}