{"id":"W3093926104","doi":"10.1145/3467981","title":"A Federated Learning Approach to Anomaly Detection in Smart Buildings","year":2021,"lang":"en","type":"preprint","venue":"ACM Transactions on Internet of Things","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Anomaly detection; Baseline (sea); Task (project management); Internet of Things; Machine learning; Convergence (economics); Building automation; Efficient energy use; Artificial intelligence; Federated learning; Computer security; Systems engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001976532,0.0006541948,0.001082555,0.0007618026,0.0005268634,0.0008633886,0.00181783,0.001232387,0.0007094655],"category_scores_gemma":[0.004911135,0.0003805549,0.0006551439,0.0008919894,0.0008443418,0.002156461,0.001449648,0.001687588,0.0002410238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008800049,"about_ca_system_score_gemma":0.001140456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004310685,"about_ca_topic_score_gemma":0.004322945,"domain_scores_codex":[0.9990263,0.0003031064,0.00006097752,0.0002931326,0.0001895639,0.0001269492],"domain_scores_gemma":[0.9980399,0.0007935094,0.0001915234,0.000393554,0.0004769714,0.0001045835],"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":[0.0002263749,0.0003331337,0.003745207,0.00005446273,0.00008916973,0.0001326205,0.0001346104,0.7681336,0.004088098,0.004496192,0.001339721,0.2172268],"study_design_scores_gemma":[0.000003943876,0.00002229722,0.0001853485,0.000001907518,0.000004618129,0.00001601477,0.000007848527,0.9945443,0.0008938264,0.00417658,0.0001395437,0.000003752411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05035055,0.000214252,0.9470971,0.000262946,0.00004766137,0.00002791761,0.00006603617,0.001471192,0.0004622138],"genre_scores_gemma":[0.8670303,0.0001228521,0.1312331,0.0001427023,0.00005100941,0.00005626054,0.0002077439,0.00005612014,0.001099873],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004310685,"threshold_uncertainty_score":0.01045305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01795899356818903,"score_gpt":0.2524868078945012,"score_spread":0.2345278143263121,"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."}}