{"id":"W4400087718","doi":"10.1016/j.adhoc.2024.103578","title":"An innovative multi-agent approach for robust cyber–physical systems using vertical federated learning","year":2024,"lang":"en","type":"article","venue":"Ad Hoc Networks","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"King Saud University; Council of Scientific and Industrial Research, India","keywords":"Cyber-physical system; Computer science; Federated learning; Distributed computing; Operating system","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":["scholarly_communication","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0005527252,0.0002675183,0.0003102249,0.0001314037,0.0003546459,0.001120384,0.006295856,0.0002377037,8.882086e-7],"category_scores_gemma":[0.001497645,0.0002428543,0.00006462987,0.001474201,0.0001192806,0.000874645,0.01049112,0.0008146616,0.000006983784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002064802,"about_ca_system_score_gemma":0.00009563209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001204166,"about_ca_topic_score_gemma":0.000001708117,"domain_scores_codex":[0.9976838,0.0001635808,0.0003151176,0.0009323478,0.0002644301,0.0006407639],"domain_scores_gemma":[0.997116,0.0002990754,0.00005522099,0.00225196,0.0001807518,0.00009701742],"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":[0.00003351907,0.0003471641,0.0001511807,0.0001501633,0.0002228186,0.00006886011,0.0004744941,0.8932437,0.001242711,0.00804575,0.01369167,0.08232794],"study_design_scores_gemma":[0.0002320284,0.0001483605,0.00004045379,0.0001064378,0.0000156047,0.00002002647,0.00009548622,0.9973573,0.0001478333,0.0005283523,0.00100912,0.000298971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02052289,0.00160565,0.9742976,0.0003635757,0.000800835,0.0004969738,0.000006121232,0.001851277,0.00005502424],"genre_scores_gemma":[0.600029,0.00002246444,0.3995113,0.00003531215,0.0002090866,0.00007374736,0.00006584617,0.00003446827,0.00001878343],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5795061,"threshold_uncertainty_score":0.9999166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07001147861332492,"score_gpt":0.3124932675006178,"score_spread":0.2424817888872929,"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."}}