{"id":"W7127147727","doi":"10.1109/trustcom66490.2025.00206","title":"PACT: A Passive Accuracy-Based Trust Metric for Malicious Client Detection in Federated Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Pact; Federated learning; Adversary; Trustworthiness; Metric (unit); Protocol (science); Scheme (mathematics); Trusted third party","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":[0.007317463,0.001683267,0.002070461,0.001652622,0.001255032,0.002738336,0.003442395,0.002062716,0.00109329],"category_scores_gemma":[0.04035467,0.000582747,0.0006867471,0.001353773,0.002213199,0.006991778,0.004303818,0.003149708,0.0009529891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00242468,"about_ca_system_score_gemma":0.002816506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00235968,"about_ca_topic_score_gemma":0.002597241,"domain_scores_codex":[0.9923033,0.002139617,0.0006312779,0.001489975,0.002832582,0.0006032359],"domain_scores_gemma":[0.9771456,0.00706589,0.003018617,0.007364796,0.004404841,0.001000315],"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":[0.00151254,0.0006922261,0.04324334,0.0004884837,0.0003962067,0.0004773443,0.0005794727,0.4093616,0.02139979,0.02362763,0.01723408,0.4809873],"study_design_scores_gemma":[0.00002256178,0.0001904469,0.001462598,0.00002293669,0.00002895631,0.0002826121,0.00005103313,0.9760417,0.008684733,0.01191818,0.001257668,0.00003651433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1034871,0.00108075,0.8825915,0.0006985372,0.0001600099,0.0002516879,0.0004664407,0.008464671,0.002799382],"genre_scores_gemma":[0.9208256,0.0001790118,0.07653648,0.0002179401,0.00006666491,0.0001175339,0.0005443128,0.000205149,0.001307239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007317463,"threshold_uncertainty_score":0.03869891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02502913349299768,"score_gpt":0.2999232009886028,"score_spread":0.2748940674956051,"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."}}