{"id":"W4381327767","doi":"10.1109/jiot.2023.3287799","title":"CRS: A Privacy-Preserving Two-Layered Distributed Machine Learning Framework for IoV","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; Canada Foundation for Innovation","keywords":"Computer science; Overhead (engineering); Vehicular ad hoc network; Computer network; Architecture; Distributed computing; Network packet; Cryptography; Wireless ad hoc network; The Internet; Server; Computer security; Wireless; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch","metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.002191723,0.0003250794,0.0005015861,0.0005326267,0.0002536644,0.0006597736,0.03767044,0.0002472471,0.0000478457],"category_scores_gemma":[0.06149202,0.0003004735,0.0002978618,0.001020154,0.0001189901,0.001732604,0.04182281,0.001785022,0.00004285197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001674138,"about_ca_system_score_gemma":0.00009063245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001079449,"about_ca_topic_score_gemma":0.000003157577,"domain_scores_codex":[0.9968008,0.000168317,0.0008845573,0.0005957353,0.0007263548,0.0008242346],"domain_scores_gemma":[0.9935307,0.001209954,0.000918802,0.003827759,0.0003345949,0.0001781952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000391097,0.0003543249,0.009975181,0.0004985283,0.001110332,0.0005736997,0.005605012,0.004317239,0.02657199,0.0156585,0.8684528,0.06649125],"study_design_scores_gemma":[0.0005739687,0.000193607,0.0001406788,0.0005381357,0.00001473506,0.0001713887,0.00005848637,0.5987247,0.01594422,0.3796305,0.003757777,0.0002517942],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05558786,0.000218718,0.9254892,0.01542458,0.001800697,0.0002229839,0.00003490527,0.001134473,0.00008655703],"genre_scores_gemma":[0.5362332,0.00008411692,0.4631545,0.0001527814,0.0001726572,0.00001787859,0.00002021639,0.00003825052,0.000126328],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8646951,"threshold_uncertainty_score":0.9999447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04310625670370935,"score_gpt":0.3141740027741652,"score_spread":0.2710677460704559,"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."}}