{"id":"W4410493037","doi":"10.1109/wispnet64060.2025.11004967","title":"Federated Learning for Anomaly Detection in Smart Cities","year":2025,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0005963715,0.00003270632,0.00006670469,0.0001198437,0.0006240768,0.0000815501,0.00003882729,0.0000473806,0.0002128721],"category_scores_gemma":[0.0004815604,0.00003383484,0.00003829369,0.000392619,0.00005529303,0.00006795303,0.00000465809,0.00005533062,0.00000682653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009156291,"about_ca_system_score_gemma":0.0001256776,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02753329,"about_ca_topic_score_gemma":0.397687,"domain_scores_codex":[0.9994982,0.0001193841,0.0001067928,0.0001055794,0.00006058448,0.0001094497],"domain_scores_gemma":[0.9996127,0.0002515533,0.00001738726,0.00002881785,0.000073961,0.00001550821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000107041,0.0001916985,0.4198285,0.0001035006,0.00008418388,8.225512e-7,0.01708332,0.002991266,0.001923558,0.05535429,0.0007819787,0.5015498],"study_design_scores_gemma":[0.002114434,0.0002327356,0.2370587,0.0001306035,0.0001391184,1.055455e-7,0.217846,0.1927573,0.01880103,0.02682842,0.3033046,0.0007868851],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8091929,0.00003576225,0.0989401,0.001694539,0.0001023916,0.00025199,3.475173e-7,0.0001114498,0.08967054],"genre_scores_gemma":[0.973543,0.000005373302,0.00003026781,0.0001350542,0.00002551172,0.00003973213,0.000003397328,0.000001443068,0.02621617],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5007629,"threshold_uncertainty_score":0.9789425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01601939221176923,"score_gpt":0.3016009758080754,"score_spread":0.2855815835963062,"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."}}