{"id":"W7151571617","doi":"10.1109/icmla66185.2025.00038","title":"A Generative Adversarial based Approach for Continual Federated Learning with Non-IID Data","year":2025,"lang":"","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Federated learning; Adversarial system; Generative grammar; Key (lock); Scheme (mathematics); Perspective (graphical)","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":[0.00302699,0.001041988,0.001063489,0.0005011415,0.0005368215,0.0009255132,0.002800767,0.001313879,0.001825601],"category_scores_gemma":[0.006205158,0.0005269836,0.000728531,0.0005641733,0.001826164,0.002332316,0.002344747,0.002875418,0.0005801625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00146191,"about_ca_system_score_gemma":0.001073446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003554866,"about_ca_topic_score_gemma":0.004423659,"domain_scores_codex":[0.9989039,0.0003575766,0.00004357566,0.0003246875,0.0002413681,0.0001289127],"domain_scores_gemma":[0.9972135,0.001348727,0.0001929722,0.0007170117,0.0003862689,0.0001416211],"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":[0.0001128033,0.00008416495,0.001042735,0.00003594612,0.00004420891,0.00009253183,0.00007739189,0.9361706,0.001742078,0.009797794,0.00240195,0.04839777],"study_design_scores_gemma":[0.000004160036,0.00001546357,0.00004691583,0.000002960955,0.000002719656,0.00001887409,0.000003754324,0.9943789,0.0004917884,0.004814889,0.0002156069,0.000004068376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01955356,0.0002386844,0.9766868,0.0004050237,0.00004640272,0.00006313635,0.00009624224,0.001403984,0.001506005],"genre_scores_gemma":[0.8660244,0.0001446527,0.1276643,0.0005498623,0.00007078335,0.0001858203,0.0003589773,0.0001720012,0.004829089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003554866,"threshold_uncertainty_score":0.01600844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04664483419893097,"score_gpt":0.2966344205856491,"score_spread":0.2499895863867181,"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."}}