{"id":"W4399358245","doi":"10.1016/j.cose.2024.103936","title":"FedIMP: Parameter Importance-based Model Poisoning attack against Federated learning system","year":2024,"lang":"en","type":"article","venue":"Computers & Security","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brock University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Boosting (machine learning); Federated learning; Vulnerability (computing); Convergence (economics); Similarity (geometry); Computer security; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001189588,0.0006240011,0.0007617096,0.0005137341,0.0004868063,0.0006505229,0.001044481,0.001416637,0.001990212],"category_scores_gemma":[0.003700827,0.0002342553,0.0006030989,0.0003075612,0.0006497072,0.001388043,0.002152585,0.001501996,0.0004311973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005058082,"about_ca_system_score_gemma":0.0005774304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007245333,"about_ca_topic_score_gemma":0.0004946311,"domain_scores_codex":[0.9992324,0.0001899855,0.00004764359,0.0001304453,0.0002734883,0.0001260577],"domain_scores_gemma":[0.998921,0.0003410872,0.0001042896,0.0004322045,0.000155684,0.00004578381],"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":[0.002358594,0.0003679124,0.006954913,0.000223975,0.0004846663,0.001345078,0.000246543,0.5644408,0.06428035,0.03245071,0.01368095,0.3131656],"study_design_scores_gemma":[0.00002768693,0.0001440458,0.0003739637,0.000009347506,0.00002245415,0.0002349646,0.00001118236,0.9736074,0.01761397,0.007234006,0.000709077,0.00001189962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1501481,0.0003577845,0.834819,0.0009019028,0.0002025081,0.0001354066,0.0002175373,0.009234537,0.003983192],"genre_scores_gemma":[0.9609199,0.00005251104,0.03699365,0.000174924,0.00001715414,0.00003126482,0.0001148177,0.00006124671,0.001634468],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001990212,"threshold_uncertainty_score":0.006657898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01843278694387394,"score_gpt":0.2659551696673857,"score_spread":0.2475223827235118,"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."}}