{"id":"W4396605314","doi":"10.1109/tce.2024.3396723","title":"A Federated Unlearning-Based Secure Management Scheme to Enable Automation in Smart Consumer Electronics Facilitated by Digital Twin","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Electronics; Scheme (mathematics); Automation; Computer science; Smart card; Embedded system; Engineering; Computer security; Electrical 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001602672,0.0003568073,0.0006508494,0.0006564533,0.001038658,0.001380265,0.00175866,0.0009130664,0.003872817],"category_scores_gemma":[0.002860223,0.0001859467,0.0003860715,0.000804966,0.001259634,0.003610983,0.002555537,0.0009920814,0.0009354366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009985784,"about_ca_system_score_gemma":0.001314997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000995094,"about_ca_topic_score_gemma":0.0008612794,"domain_scores_codex":[0.9982413,0.0004205593,0.0001764036,0.0004159701,0.000464831,0.0002810489],"domain_scores_gemma":[0.9978881,0.0003364286,0.000247311,0.0009617241,0.0004004367,0.0001659204],"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":[0.002532873,0.000805366,0.003868968,0.0002618588,0.0001057717,0.001060093,0.0009087788,0.123824,0.05377516,0.1935829,0.01054177,0.6087325],"study_design_scores_gemma":[0.0000996774,0.0004519609,0.0006475573,0.00004973474,0.00004790146,0.000597814,0.0001019407,0.8904768,0.04686482,0.04943645,0.01116173,0.00006365558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1082745,0.0002921148,0.8788109,0.0005793039,0.0001560402,0.0002414052,0.000130986,0.003313675,0.008201059],"genre_scores_gemma":[0.9408067,0.00007394602,0.05398704,0.000184972,0.00002580479,0.00008295908,0.00008924927,0.00003802953,0.004711337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003872817,"threshold_uncertainty_score":0.01295584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006186399604651391,"score_gpt":0.229750641073692,"score_spread":0.2235642414690406,"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."}}