{"id":"W7125014675","doi":"10.1109/flta67013.2025.11336384","title":"Flashback: Understanding and Mitigating Forgetting in Federated Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"King Abdullah University of Science and Technology","keywords":"Forgetting; Federated learning; Overhead (engineering); Metric (unit); Measure (data warehouse); Data modeling; Distributed learning","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":[0.006563575,0.00122839,0.001548739,0.001007729,0.0009340953,0.001771068,0.003487172,0.001722689,0.0007428798],"category_scores_gemma":[0.02478075,0.0006210725,0.000595761,0.001052724,0.00196469,0.006561738,0.003139032,0.00245602,0.0002614429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001418224,"about_ca_system_score_gemma":0.001820544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004529702,"about_ca_topic_score_gemma":0.004074019,"domain_scores_codex":[0.9974039,0.0007748311,0.000236883,0.0006815806,0.0006053729,0.000297436],"domain_scores_gemma":[0.9838656,0.008147574,0.00097873,0.005155155,0.001447956,0.0004049006],"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":[0.0009545044,0.0004879623,0.01039376,0.0001780956,0.0001542602,0.0002895386,0.0006644908,0.53154,0.006285849,0.009093395,0.002895106,0.437063],"study_design_scores_gemma":[0.00003409032,0.0001446254,0.0005321752,0.00001583877,0.00002233857,0.00009645398,0.00005835219,0.9771707,0.006520699,0.01465401,0.0007351806,0.00001544116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1007657,0.0006213413,0.8929564,0.000540983,0.00005717929,0.0000931005,0.00008553034,0.004207595,0.0006721307],"genre_scores_gemma":[0.8389241,0.0001608919,0.1590788,0.0003866547,0.00005465405,0.0001067406,0.0002083823,0.0001363122,0.0009434835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006563575,"threshold_uncertainty_score":0.0347119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04600898533131364,"score_gpt":0.2908640876537627,"score_spread":0.2448551023224491,"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."}}