{"id":"W4406657986","doi":"10.1109/ist64061.2024.10843494","title":"Federated Learning: Attacks, Defenses, Opportunities and Challenges","year":2024,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.0002951763,0.0001313946,0.0001161409,0.0001431543,0.0001338327,0.0006579703,0.004955764,0.00009423284,0.00003201369],"category_scores_gemma":[0.002417306,0.0001104207,0.00002080932,0.0001729979,0.00009694602,0.0006890592,0.03383412,0.0002411631,0.00005572287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000217378,"about_ca_system_score_gemma":0.00005609243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001687373,"about_ca_topic_score_gemma":0.00001326943,"domain_scores_codex":[0.9989451,0.00005295281,0.0001305552,0.0004621606,0.0001638308,0.0002453956],"domain_scores_gemma":[0.9980839,0.0002070184,0.0000195472,0.001597757,0.00003390391,0.00005784245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001207401,0.00001264647,0.0000488731,0.0001185178,0.00003809392,0.0002109129,0.0001138917,8.329561e-7,0.00009717712,0.1851295,0.2177225,0.5965059],"study_design_scores_gemma":[0.00009169926,0.0001174098,0.0002246076,0.00009647749,0.000006081209,0.0001580598,0.0005370842,0.2567694,0.001088975,0.1052961,0.635287,0.0003270388],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.008433937,0.08260827,0.2255564,0.5959703,0.0008495072,0.0002218158,0.000007725524,0.01571486,0.07063721],"genre_scores_gemma":[0.7660702,0.09121861,0.1376318,0.0004301045,0.00007772245,0.00003110447,0.00001629593,0.00003935543,0.004484819],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7576363,"threshold_uncertainty_score":0.9739801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1399510934537067,"score_gpt":0.2943298621120974,"score_spread":0.1543787686583907,"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."}}