{"id":"W4386869660","doi":"10.1109/tcomm.2023.3317300","title":"Joint Age-Based Client Selection and Resource Allocation for Communication-Efficient Federated Learning Over NOMA Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Resource allocation; Selection (genetic algorithm); Convergence (economics); Throughput; Wireless; Noma; Distributed computing; Computer network; Dual (grammatical number); Wireless network; Joint (building); Resource management (computing); Artificial intelligence; Telecommunications; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.0009059811,0.0002087351,0.0001967896,0.0004640954,0.002293675,0.0003444384,0.006635235,0.0001824861,0.00000565865],"category_scores_gemma":[0.0006507913,0.000235323,0.00009283491,0.001672852,0.0002072727,0.0002565338,0.0008615088,0.0007337135,0.0000212525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002044575,"about_ca_system_score_gemma":0.00008112998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008501829,"about_ca_topic_score_gemma":0.0002365852,"domain_scores_codex":[0.9981086,0.0003866061,0.0004362655,0.0004749735,0.0002370011,0.0003565477],"domain_scores_gemma":[0.9923468,0.001227124,0.0001955137,0.005962562,0.0001852794,0.00008270804],"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":[0.00003885229,0.0005632106,0.00003972394,0.00003342976,0.00009336877,8.806688e-7,0.0003871769,0.9069566,0.002510875,0.003297434,0.0165663,0.06951219],"study_design_scores_gemma":[0.0005211841,0.00009346613,0.0008041529,0.00006906427,0.00002242697,0.000003562501,0.00008805491,0.9891445,0.002222291,0.001508675,0.005294854,0.000227741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006681635,0.0001151378,0.966115,0.0240698,0.0001042388,0.000694487,0.00001592219,0.002026775,0.0001770066],"genre_scores_gemma":[0.9048906,0.0003513531,0.09363154,0.0002022368,0.0000090371,0.0006462781,0.0001350636,0.00003094904,0.0001029671],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8982089,"threshold_uncertainty_score":0.9990052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05404172855537724,"score_gpt":0.2940551864008654,"score_spread":0.2400134578454881,"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."}}