{"id":"W2886444620","doi":"10.48550/arxiv.1808.04866","title":"Mitigating Sybils in Federated Learning Poisoning","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":362,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Business; Criminology; Internet privacy; Computer science; Computer security; Psychology","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":[0.01488482,0.001210259,0.002226244,0.001663331,0.002446098,0.004183064,0.002663929,0.003377182,0.001474267],"category_scores_gemma":[0.04447693,0.0007230869,0.001221305,0.00132353,0.004477598,0.01137659,0.0110156,0.004825525,0.0007504538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001816785,"about_ca_system_score_gemma":0.002517325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008866384,"about_ca_topic_score_gemma":0.0008078372,"domain_scores_codex":[0.9838562,0.007960829,0.0008684681,0.002214325,0.003822453,0.001277683],"domain_scores_gemma":[0.9491786,0.02002473,0.003668903,0.02297302,0.002859741,0.001294903],"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":[0.002880905,0.001738615,0.02823749,0.000540469,0.0007401076,0.001362502,0.002389639,0.4886962,0.0385384,0.1239182,0.01257064,0.2983868],"study_design_scores_gemma":[0.0001036058,0.0003037714,0.0008617818,0.00004526529,0.00005202615,0.000558004,0.0001802344,0.9065678,0.02268386,0.06615681,0.002438525,0.00004829036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2559085,0.0006563929,0.7281637,0.001825364,0.0001212674,0.0003522037,0.0001486356,0.007910709,0.004913236],"genre_scores_gemma":[0.9460351,0.0000832102,0.05240305,0.0003497515,0.00003667394,0.0000873941,0.00008514798,0.0001067322,0.0008130128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01488482,"threshold_uncertainty_score":0.07871938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07659239540079592,"score_gpt":0.2143127364843953,"score_spread":0.1377203410835994,"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."}}