{"id":"W4361983774","doi":"10.1109/tnsm.2023.3263831","title":"Retracted: Communication-Efficient Personalized Federated Meta-Learning in Edge Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":34,"is_retracted":true,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"King Saud University","keywords":"Computer science; Overhead (engineering); Differential privacy; Edge device; Distributed computing; Upload; Personalization; Edge computing; Enhanced Data Rates for GSM Evolution; Autoencoder; Information privacy; Machine learning; Computer network; Artificial intelligence; Deep learning; Data mining; Cloud computing; Computer security","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002328668,0.0009445561,0.001311927,0.0005824546,0.0008143942,0.001198405,0.002448136,0.00163958,0.001375468],"category_scores_gemma":[0.005686554,0.0004652286,0.0008973834,0.0007335778,0.0009608649,0.002979548,0.002581614,0.001828142,0.00040348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009720034,"about_ca_system_score_gemma":0.001250919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003747483,"about_ca_topic_score_gemma":0.003807899,"domain_scores_codex":[0.9988075,0.0004164064,0.0000644892,0.0003186578,0.0002175292,0.0001752921],"domain_scores_gemma":[0.998118,0.0007683721,0.0001394939,0.0005627463,0.0002992588,0.0001121621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002996532,0.000126419,0.001588587,0.00006246175,0.00009265574,0.0001800902,0.000187378,0.8622032,0.002405201,0.01040487,0.002369201,0.1200803],"study_design_scores_gemma":[0.000006645842,0.00002322502,0.00005235986,0.000003289407,0.000006583008,0.00002192324,0.000009895705,0.9944112,0.0005747202,0.004662933,0.0002234081,0.000003824937],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02548952,0.0002580225,0.9722369,0.0002559566,0.00004096063,0.0000289787,0.00006024045,0.0007905567,0.0008388173],"genre_scores_gemma":[0.8626373,0.0001710674,0.133183,0.0004566746,0.00005352799,0.000102156,0.0002696889,0.000135673,0.00299081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9983604,"threshold_uncertainty_score":0.01231527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03757014228988816,"score_gpt":0.2621756283341551,"score_spread":0.224605486044267,"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."}}