{"id":"W4389609829","doi":"10.1109/ojcoms.2023.3342008","title":"A Vertical Heterogeneous Network (VHetNet)-Enabled Asynchronous Federated Learning-Based Anomaly Detection Framework for Ubiquitous IoT","year":2023,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Anomaly detection; Intrusion detection system; Distributed computing; Asynchronous communication; Software deployment; Real-time computing; Computer network; Artificial intelligence","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":[0.0007113442,0.0004622591,0.0004807303,0.0003216045,0.0004536244,0.0007966335,0.001284205,0.0005608051,0.0008257767],"category_scores_gemma":[0.001000301,0.0001779888,0.0004714563,0.0003355725,0.0005005076,0.0009672808,0.001254493,0.0007071671,0.0001328119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007494929,"about_ca_system_score_gemma":0.001055682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006542571,"about_ca_topic_score_gemma":0.006644058,"domain_scores_codex":[0.9996243,0.0000766915,0.00002228974,0.0001168104,0.0000980817,0.00006182998],"domain_scores_gemma":[0.9997252,0.00008732273,0.00004270222,0.00002976346,0.00007683461,0.00003824033],"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":[0.00009045209,0.00006111756,0.001284598,0.0000476122,0.00003527305,0.0002311333,0.00007777954,0.9074795,0.004970375,0.02997851,0.001351487,0.05439227],"study_design_scores_gemma":[0.000003165351,0.00001095356,0.0000553641,0.000001778353,0.000003641605,0.00001051744,0.000005463928,0.9967128,0.0003199061,0.002477035,0.0003973095,0.000001978874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01051528,0.0001132875,0.9875979,0.00009761756,0.00003480074,0.00002314531,0.00003170526,0.0003401486,0.001246241],"genre_scores_gemma":[0.8186191,0.0001962027,0.1786759,0.00011586,0.00003364755,0.0001114687,0.000190093,0.00004843815,0.002009359],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006542571,"threshold_uncertainty_score":0.01300895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02922947439633915,"score_gpt":0.2862105617393312,"score_spread":0.2569810873429921,"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."}}