{"id":"W4383221436","doi":"10.1145/3579856.3595790","title":"Going Haywire: False Friends in Federated Learning and How to Find Them","year":2023,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Backdoor; Computer science; Outlier; Anomaly detection; Reputation; Reliability (semiconductor); Deep learning; Curse of dimensionality; Set (abstract data type); Artificial intelligence; Computer security; Data mining; Power (physics)","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":[0.006010209,0.001407667,0.001830849,0.001240615,0.002428297,0.003720082,0.00271055,0.004051622,0.002749306],"category_scores_gemma":[0.02911338,0.0009369369,0.000854664,0.0007616314,0.004462561,0.01607306,0.005943655,0.005070512,0.001418372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001317537,"about_ca_system_score_gemma":0.001141091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001945785,"about_ca_topic_score_gemma":0.002454286,"domain_scores_codex":[0.9953577,0.001628765,0.0001962748,0.001122783,0.001152499,0.0005419968],"domain_scores_gemma":[0.9888111,0.004207419,0.001048523,0.004118769,0.001131564,0.0006825737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002219025,0.0006526447,0.02698414,0.0003309336,0.0004284316,0.001416078,0.002828775,0.1727393,0.01040309,0.176968,0.0564599,0.5485696],"study_design_scores_gemma":[0.00006993441,0.0002092243,0.0007613933,0.0000959042,0.00005315389,0.0006670121,0.0005996967,0.8294881,0.010141,0.1474477,0.01039748,0.00006934055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1264475,0.001969927,0.8456106,0.0102574,0.000486296,0.0001510942,0.0002015734,0.009366908,0.005508638],"genre_scores_gemma":[0.8408302,0.000423596,0.1508416,0.001765898,0.000192259,0.00009017091,0.0002703924,0.0005792202,0.005006689],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006010209,"threshold_uncertainty_score":0.03178537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02198216705812786,"score_gpt":0.2637401798576258,"score_spread":0.241758012799498,"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."}}