{"id":"W4402302652","doi":"10.1109/tvt.2024.3456029","title":"A Federated Meta Learning-Based Secure Data Consolidation Scheme for Industrial AIoT Leveraging Drone","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Drone; Scheme (mathematics); Consolidation (business); Computer science; Engineering; Computer network; Computer security; Business","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":[0.001086489,0.0004482425,0.0008706361,0.0007083659,0.0009826247,0.001182163,0.001645388,0.0008482068,0.001644667],"category_scores_gemma":[0.002225624,0.0001943777,0.0006284404,0.001044066,0.0007450255,0.003257213,0.002713582,0.000852193,0.0005202465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009860778,"about_ca_system_score_gemma":0.00134488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001343963,"about_ca_topic_score_gemma":0.001508444,"domain_scores_codex":[0.9986676,0.0002846961,0.0001443162,0.0003195717,0.0003425644,0.0002413207],"domain_scores_gemma":[0.9985412,0.0002364035,0.0001912351,0.0006433149,0.0002719218,0.0001158898],"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":[0.001529401,0.0005155531,0.004274146,0.000190444,0.0001828377,0.0006963708,0.0004964417,0.3749669,0.05082221,0.06797299,0.005289867,0.4930628],"study_design_scores_gemma":[0.00003704418,0.0001853391,0.00032124,0.00001362852,0.00002256778,0.000235895,0.00006665264,0.9659082,0.01631067,0.01437192,0.002501748,0.00002510511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07714866,0.0003193763,0.9180835,0.0002783103,0.0000509205,0.0001392855,0.0001265888,0.001294539,0.002558738],"genre_scores_gemma":[0.9376413,0.00009739609,0.05969467,0.000116719,0.00001474515,0.00008180233,0.0002176728,0.00002273402,0.002112924],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001645388,"threshold_uncertainty_score":0.007154584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0993011948530566,"score_gpt":0.3086075058278644,"score_spread":0.2093063109748078,"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."}}