{"id":"W4416733312","doi":"10.1016/j.knosys.2025.114951","title":"FedPAD: Aggregation-free federated learning with prototype-based adaptive distillation","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Federated learning; Robustness (evolution); Leverage (statistics); Concept drift; Benchmark (surveying); Feature (linguistics); Convergence (economics); Tuple","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":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.0007905691,0.000363728,0.0004000814,0.0005178032,0.0006032572,0.0006548344,0.009533504,0.0002431493,0.000006079942],"category_scores_gemma":[0.008329903,0.0003133786,0.00007058804,0.002102431,0.0001632319,0.0004679747,0.005357984,0.0004777618,0.00008390858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000447814,"about_ca_system_score_gemma":0.0009340473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00013899,"about_ca_topic_score_gemma":0.0001552042,"domain_scores_codex":[0.997192,0.0004713064,0.0005003875,0.0009048938,0.0004487489,0.0004826707],"domain_scores_gemma":[0.9930576,0.000646995,0.0003287301,0.005209172,0.0006708055,0.00008670607],"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":[0.002859256,0.002612041,0.127196,0.006192442,0.001295118,0.0003186543,0.001088347,0.1230739,0.004424985,0.1341919,0.4116285,0.1851188],"study_design_scores_gemma":[0.001678827,0.0003762527,0.0008915485,0.001262354,0.00001992936,0.000003017797,0.00006889542,0.9813678,0.004661378,0.002649671,0.006625763,0.0003945589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003208565,0.0006671363,0.980215,0.003205694,0.0006766202,0.001869975,0.00001780536,0.0023858,0.007753452],"genre_scores_gemma":[0.9566284,0.0000014416,0.04200007,0.00004697755,0.00005539577,0.0006660367,0.00005966423,0.00002976331,0.0005122877],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9534198,"threshold_uncertainty_score":0.9999318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02111005274464568,"score_gpt":0.2601950478013772,"score_spread":0.2390849950567315,"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."}}