{"id":"W4318562013","doi":"10.1016/j.ress.2023.109130","title":"A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life","year":2023,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":70,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Prognostics; Transformer; Raw data; Architecture; Computer science; Asset management; Federated learning; Data mining; Reliability engineering; Distributed computing; Engineering; Finance","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":[0.001907065,0.0003847849,0.0006030253,0.0004082754,0.0002831832,0.0006654749,0.0007287973,0.0005170736,0.0005562728],"category_scores_gemma":[0.003239792,0.0001289652,0.0003484169,0.0004241408,0.000253838,0.001197039,0.000385956,0.0003759506,0.0001035254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005052896,"about_ca_system_score_gemma":0.0007780805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005525763,"about_ca_topic_score_gemma":0.004076748,"domain_scores_codex":[0.9995509,0.0001506235,0.0000251454,0.0001007227,0.0001200947,0.00005247902],"domain_scores_gemma":[0.9979638,0.001095466,0.0001050423,0.0002783974,0.0005088211,0.00004855612],"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":[0.000868187,0.0003254031,0.007003452,0.00009527365,0.0001439487,0.00006754238,0.00007392556,0.7404016,0.005144051,0.002553686,0.0005617049,0.2427612],"study_design_scores_gemma":[0.00001102961,0.0001156052,0.001207347,0.000003658235,0.00002170447,0.00002480296,0.00002333469,0.9961338,0.001763121,0.0005797416,0.0001117363,0.000004107216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4668632,0.0007072358,0.5287466,0.0001263693,0.00005514335,0.00005773663,0.00006932097,0.0007181456,0.00265621],"genre_scores_gemma":[0.9778634,0.00008398869,0.02152896,0.00001179825,0.00001002686,0.000008513133,0.00004263291,0.00001025988,0.0004404312],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005525763,"threshold_uncertainty_score":0.01098722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05113267390719354,"score_gpt":0.2729952128577262,"score_spread":0.2218625389505327,"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."}}