{"id":"W4404036747","doi":"10.1109/tifs.2024.3490861","title":"Evaluating Security and Robustness for Split Federated Learning Against Poisoning Attacks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Queen's University","funders":"","keywords":"Computer science; Robustness (evolution); Computer security","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":[0.01129759,0.001119276,0.0009248371,0.001274545,0.0009129986,0.001629258,0.001589119,0.001991785,0.0007139453],"category_scores_gemma":[0.04366061,0.0003577254,0.0009251032,0.0007710411,0.002581993,0.003444922,0.003064018,0.001874556,0.000249511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001719875,"about_ca_system_score_gemma":0.001825224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002773635,"about_ca_topic_score_gemma":0.001400867,"domain_scores_codex":[0.9921985,0.003241733,0.0005695165,0.001303434,0.001828368,0.0008584571],"domain_scores_gemma":[0.9638706,0.02064106,0.003235454,0.008741274,0.002441217,0.001070515],"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":[0.00168457,0.0003888986,0.02286435,0.0001973006,0.0003189581,0.0001511349,0.000169947,0.9072881,0.00758543,0.008458524,0.001403485,0.04948926],"study_design_scores_gemma":[0.00004726736,0.0002913003,0.00125021,0.00002166382,0.00002700298,0.0000690728,0.0000669017,0.9873958,0.006531022,0.004030697,0.0002541344,0.00001497876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8365387,0.001136372,0.1553323,0.0009804682,0.0001226972,0.000239008,0.000362063,0.002287735,0.003000649],"genre_scores_gemma":[0.9816611,0.00008797848,0.01768226,0.000074474,0.00001187737,0.00004308901,0.000183076,0.00003416073,0.0002221128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01129759,"threshold_uncertainty_score":0.05974805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03187958657872434,"score_gpt":0.3032457607121363,"score_spread":0.271366174133412,"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."}}