{"id":"W4400076000","doi":"10.1109/tifs.2024.3420126","title":"A Robust Privacy-Preserving Federated Learning Model Against Model 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":319,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University; University of Calgary; University of Guelph","funders":"","keywords":"Computer science; Computer security; Data modeling; Privacy protection; Information privacy; Internet privacy; Database","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":[0.003508146,0.0007601047,0.001501816,0.0008246333,0.0007683738,0.002160497,0.002991169,0.001821044,0.001405827],"category_scores_gemma":[0.008913195,0.0004261599,0.001033177,0.0009801595,0.001778775,0.004590895,0.002786433,0.00204229,0.0006595907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00145145,"about_ca_system_score_gemma":0.002290113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001828284,"about_ca_topic_score_gemma":0.001218316,"domain_scores_codex":[0.9971783,0.0007901447,0.0001665922,0.0007239566,0.0008298061,0.000311175],"domain_scores_gemma":[0.9951467,0.001514113,0.0006703112,0.001644089,0.0008340408,0.0001907629],"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":[0.0003134761,0.0001398897,0.001192454,0.00006259251,0.00007164184,0.0002064305,0.0001243265,0.8786982,0.004600761,0.05678089,0.001481958,0.05632744],"study_design_scores_gemma":[0.000007638172,0.00002904959,0.00005399177,0.000004639019,0.000006969973,0.00005014965,0.000007263727,0.9854004,0.001387483,0.01276743,0.0002774212,0.000007554212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01989599,0.0001186806,0.9779286,0.00035438,0.00001930918,0.00005595338,0.00007590025,0.0006361672,0.00091504],"genre_scores_gemma":[0.8847572,0.0001769886,0.1107933,0.0001907282,0.00003068499,0.0001176242,0.0001590424,0.00007522424,0.003699272],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003508146,"threshold_uncertainty_score":0.01855308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03051808248658837,"score_gpt":0.2523782381470152,"score_spread":0.2218601556604268,"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."}}