{"id":"W4401108970","doi":"10.1109/ticps.2024.3435178","title":"IP2FL: Interpretation-Based Privacy-Preserving Federated Learning for Industrial Cyber-Physical Systems","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Cyber-Physical Systems","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brandon University; University of Guelph","funders":"","keywords":"Cyber-physical system; Interpretation (philosophy); Computer science; Computer security; Information privacy; Internet privacy; Federated 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":[0.005218598,0.0006263076,0.001029826,0.0009060818,0.0008500172,0.002401247,0.002473279,0.001995979,0.002370398],"category_scores_gemma":[0.008217089,0.000363134,0.001386138,0.001193181,0.001756091,0.00495029,0.003833521,0.002583704,0.0005655147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002103615,"about_ca_system_score_gemma":0.002309166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001739061,"about_ca_topic_score_gemma":0.001576885,"domain_scores_codex":[0.9957546,0.002030038,0.0002015045,0.000604576,0.001103604,0.0003056686],"domain_scores_gemma":[0.9961815,0.001345609,0.0003056314,0.001557352,0.0004748954,0.0001349708],"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":[0.0003683237,0.0003702939,0.001704785,0.0001491857,0.0001108261,0.0002613939,0.0002817784,0.5352407,0.003811298,0.1486763,0.00710636,0.3019188],"study_design_scores_gemma":[0.00001961703,0.00006959308,0.0001386037,0.00001113115,0.000008028281,0.00006558515,0.00002167417,0.9123323,0.002301208,0.08307196,0.00195028,0.00001007289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007528391,0.00008579934,0.9895092,0.000431312,0.00001842098,0.00004784405,0.0000672159,0.0007591371,0.001552643],"genre_scores_gemma":[0.5805477,0.0002407592,0.4131081,0.0004442879,0.00007434861,0.000207422,0.0005422304,0.0001367302,0.0046985],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005218598,"threshold_uncertainty_score":0.02759892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05987463834522531,"score_gpt":0.2969203516925115,"score_spread":0.2370457133472862,"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."}}