{"id":"W4409880074","doi":"10.1016/j.comnet.2025.111340","title":"MFTE: Multifactor and fuzzy trust evaluation for federated learning in mobile edge computing","year":2025,"lang":"en","type":"article","venue":"Computer Networks","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Edge computing; Enhanced Data Rates for GSM Evolution; Fuzzy logic; Mobile edge computing; Artificial intelligence; Distributed computing","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":["open_science"],"consensus_categories":[],"category_scores_codex":[0.001167862,0.0002152226,0.0002885126,0.0002453399,0.0002965406,0.0004622969,0.004770914,0.0002157145,0.000001894622],"category_scores_gemma":[0.001519089,0.0002259906,0.00004429763,0.0007276512,0.00006633053,0.0003846185,0.02128678,0.0004653631,0.000002138195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000142194,"about_ca_system_score_gemma":0.00008000703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001217611,"about_ca_topic_score_gemma":0.00001351711,"domain_scores_codex":[0.9980521,0.0001853295,0.0003831729,0.0007455755,0.0001750525,0.0004587583],"domain_scores_gemma":[0.9973343,0.0006902463,0.0001300249,0.001641547,0.0001583233,0.00004557457],"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":[0.000009444947,0.00003997585,0.005793628,0.00003211079,0.00002168101,0.000002880027,0.00008619145,0.06827363,0.00001940572,0.0004534814,0.02198763,0.90328],"study_design_scores_gemma":[0.0009952813,0.00007479815,0.007623038,0.0001615813,0.00000766263,0.000002883689,0.00001391812,0.9805232,0.00006029071,0.008128605,0.002201091,0.0002076463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05477988,0.00119573,0.9400681,0.001147082,0.001088202,0.0008941154,0.000001384416,0.0006780676,0.0001473994],"genre_scores_gemma":[0.7345317,0.00004559557,0.2649239,0.0002209319,0.0001253857,0.00008321798,0.00003458043,0.00001229341,0.00002238315],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9122496,"threshold_uncertainty_score":0.9866289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02768545864808804,"score_gpt":0.3026497872618097,"score_spread":0.2749643286137217,"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."}}