{"id":"W3154459044","doi":"10.1109/jiot.2021.3074382","title":"Toward Accurate Anomaly Detection in Industrial Internet of Things Using Hierarchical Federated Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":245,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"King Saud University","keywords":"Anomaly detection; Computer science; Federated learning; Reinforcement learning; Industrial Internet; The Internet; Artificial intelligence; Internet of Things; Computer security; Machine learning; Data mining; World Wide Web","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":[0.002065868,0.0006684112,0.001091701,0.0007991345,0.0006200552,0.0009297857,0.001444545,0.0008315017,0.0003481572],"category_scores_gemma":[0.006151528,0.0003056361,0.000670324,0.0006457318,0.000855151,0.001869385,0.001557627,0.001299859,0.0001289379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008856332,"about_ca_system_score_gemma":0.00119765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002989159,"about_ca_topic_score_gemma":0.002568416,"domain_scores_codex":[0.9985614,0.0003682595,0.0000912302,0.0004204727,0.0003734507,0.0001852753],"domain_scores_gemma":[0.9970639,0.001152246,0.0003994429,0.0005892707,0.0006587201,0.0001363969],"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":[0.0002960056,0.0003800243,0.01434654,0.00007002103,0.0001457676,0.0002216248,0.0001652049,0.7437505,0.009779687,0.00862163,0.001341896,0.2208812],"study_design_scores_gemma":[0.000003349265,0.00001947241,0.0002305935,0.000001517376,0.000004200198,0.00002144536,0.000008141295,0.9952809,0.001083209,0.003262222,0.00008156687,0.000003325959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06238074,0.0001167006,0.9357985,0.0001761182,0.00002223186,0.00003145479,0.00002904824,0.00102363,0.0004216141],"genre_scores_gemma":[0.9230409,0.00005498812,0.07615251,0.000105365,0.0000145012,0.00003676697,0.00008432433,0.00002667622,0.0004839345],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002989159,"threshold_uncertainty_score":0.01092547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07706288089800138,"score_gpt":0.2980538154561173,"score_spread":0.2209909345581159,"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."}}