{"id":"W4401870871","doi":"10.1109/jiot.2024.3446725","title":"AEFL: Anonymous and Efficient Federated Learning in Vehicle–Road Cooperation Systems With Augmented Intelligence of Things","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"National Natural Science Foundation of China","keywords":"Computer science; Intelligent transportation system; Computer security; Transport engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.001344447,0.000186781,0.0003077395,0.0004668232,0.00007203969,0.0007400566,0.004303875,0.0001169787,0.000005320635],"category_scores_gemma":[0.00164937,0.0001470025,0.00003864532,0.0005973115,0.0001496437,0.001199102,0.003671588,0.0009579532,0.000003830117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001461259,"about_ca_system_score_gemma":0.0001165497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004474126,"about_ca_topic_score_gemma":0.000005759216,"domain_scores_codex":[0.9980571,0.0001447744,0.0006792307,0.0003824801,0.0004645523,0.0002718063],"domain_scores_gemma":[0.9985916,0.000165047,0.0003489777,0.0006003931,0.0002327557,0.0000612196],"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":[0.0008741744,0.001000691,0.006769242,0.002919413,0.00117572,0.003250454,0.04758979,0.1216228,0.3878645,0.01188396,0.02004039,0.3950089],"study_design_scores_gemma":[0.0001887899,0.0003963046,0.00008343945,0.001962183,0.000009792932,0.0007280898,0.0002388316,0.9522376,0.04317142,0.0007811273,0.00005634879,0.0001460989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4902534,0.0009596388,0.5069679,0.0009345009,0.0004781646,0.0001175176,7.274458e-7,0.0001752038,0.0001129269],"genre_scores_gemma":[0.984914,0.0001050296,0.01482836,0.00003824943,0.00001960441,0.000003871342,0.00000104169,0.00001559382,0.0000742341],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8306147,"threshold_uncertainty_score":0.7997743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01787890884889296,"score_gpt":0.2593174523333811,"score_spread":0.2414385434844881,"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."}}