{"id":"W4387883806","doi":"10.1109/icc45041.2023.10278789","title":"Reliable Federated Learning for Age Sensitive Mobile Edge Computing Systems","year":2023,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Upload; Computer science; Enhanced Data Rates for GSM Evolution; Process (computing); Mobile edge computing; Edge computing; Distributed computing; Artificial intelligence; Machine learning; Data mining","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":[0.003123031,0.0009633059,0.001290775,0.0006812361,0.0009438845,0.001509174,0.002301393,0.00130296,0.001222093],"category_scores_gemma":[0.006861747,0.0003457333,0.0004731398,0.001080998,0.001016846,0.003819765,0.002638846,0.001495337,0.0003744793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001037626,"about_ca_system_score_gemma":0.001321396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001761729,"about_ca_topic_score_gemma":0.002027234,"domain_scores_codex":[0.9983993,0.0004792796,0.00009101865,0.0004725353,0.0003059632,0.0002517608],"domain_scores_gemma":[0.9968191,0.001202275,0.000363115,0.0009912561,0.0004503338,0.0001738007],"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":[0.0004889056,0.0002344497,0.0028836,0.0001160937,0.00007449953,0.000250643,0.0001848487,0.8022922,0.004333836,0.01786239,0.003972001,0.1673064],"study_design_scores_gemma":[0.000006027161,0.00004013747,0.0001983122,0.000004995739,0.000006649342,0.00005604653,0.00003264424,0.9843907,0.001493196,0.01315757,0.0006068571,0.000006892812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03931753,0.0004815754,0.9573003,0.0004145892,0.0000559752,0.00006624476,0.0001548261,0.001097611,0.001111332],"genre_scores_gemma":[0.9169841,0.0001867608,0.0808581,0.0002021655,0.0000534059,0.00007143017,0.0002126445,0.00005809393,0.001373227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003123031,"threshold_uncertainty_score":0.01651639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03869250358962828,"score_gpt":0.2892248214660368,"score_spread":0.2505323178764086,"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."}}