{"id":"W3132804183","doi":"10.1109/mnet.011.2000552","title":"Making a Case for Federated Learning in the Internet of Vehicles and Intelligent Transportation Systems","year":2021,"lang":"en","type":"preprint","venue":"IEEE Network","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Enabling; Computer science; Scalability; Intelligent transportation system; Virtualization; The Internet; Key (lock); Software-defined networking; Computer security; Cloud computing; Computer network; World Wide Web; Engineering; 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":[0.001021824,0.0001780595,0.000311407,0.00008137825,0.00007492166,0.0004161182,0.005151578,0.000240852,3.287846e-7],"category_scores_gemma":[0.0009108356,0.0001522859,0.00005720998,0.0002920394,0.00005339218,0.0001210806,0.004762964,0.0006563509,1.961418e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004758116,"about_ca_system_score_gemma":0.00005973004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003687984,"about_ca_topic_score_gemma":0.0005150482,"domain_scores_codex":[0.9983882,0.0002206213,0.0004675064,0.0005087379,0.0001684861,0.0002464223],"domain_scores_gemma":[0.9972927,0.0005227661,0.0003185825,0.001763433,0.00008698417,0.00001554861],"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":[0.0001829406,0.0002599971,0.01408111,0.005154282,0.0005531059,0.004579272,0.01821003,0.6949161,0.0002358087,0.003980806,0.1134331,0.1444135],"study_design_scores_gemma":[0.0001412205,0.00005742706,0.0003983942,0.001726991,0.000022308,0.0001527469,0.0006703656,0.9838315,0.0002945533,0.01181752,0.000672261,0.0002147704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3237416,0.002575469,0.6700428,0.0008016825,0.002013957,0.000620806,0.00001000223,0.0001719494,0.00002173748],"genre_scores_gemma":[0.9715458,0.000130044,0.02792284,0.00003739957,0.0001716091,0.0001340372,0.00004003209,0.00001272527,0.00000547816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6478043,"threshold_uncertainty_score":0.9572999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07296759880561182,"score_gpt":0.3090832888547175,"score_spread":0.2361156900491057,"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."}}