{"id":"W4402808772","doi":"10.1109/jiot.2024.3467110","title":"WFSL: Warmup-Based Federated Sequential Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; École de Technologie Supérieure","funders":"","keywords":"Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001099371,0.0001949416,0.0002335499,0.0004091983,0.0001152215,0.001354608,0.01457726,0.0001263179,0.00009709517],"category_scores_gemma":[0.003939655,0.0001698109,0.000170373,0.0003566953,0.0001106017,0.001581505,0.008911183,0.001515975,0.00007807015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001617715,"about_ca_system_score_gemma":0.000200772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006455522,"about_ca_topic_score_gemma":0.000001059958,"domain_scores_codex":[0.9980444,0.0001333318,0.0005113636,0.0003896364,0.000544988,0.0003762242],"domain_scores_gemma":[0.9980403,0.000189954,0.0002428536,0.001272163,0.000160958,0.00009373928],"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":[0.0000629532,0.0001246254,0.0003978802,0.0002205392,0.0004100115,0.002246792,0.00104625,0.001016445,0.07821154,0.001816883,0.7912264,0.1232196],"study_design_scores_gemma":[0.00020322,0.000165214,0.000009263554,0.0005254876,0.00001251699,0.0006645488,0.00001481696,0.7775477,0.1858846,0.02814583,0.006650728,0.0001760923],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06877068,0.0004220874,0.9148753,0.01073838,0.003477766,0.00005803744,0.000002009486,0.000966393,0.000689353],"genre_scores_gemma":[0.8854404,0.00002631135,0.1138406,0.0002200914,0.0001254417,0.000002112123,0.00000233333,0.00002200564,0.0003206991],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8166697,"threshold_uncertainty_score":0.9996821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03105494285700967,"score_gpt":0.2902040608865094,"score_spread":0.2591491180294997,"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."}}