{"id":"W4388666109","doi":"10.1109/twc.2023.3330967","title":"Channel-Aware Joint AoI and Diversity Optimization for Client Scheduling in Federated Learning With Non-IID Datasets","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Age of Information Optimization","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Computer science; Scalability; Scheduling (production processes); Markov decision process; Independent and identically distributed random variables; Telecommunications link; Convergence (economics); Distributed computing; Job shop scheduling; Channel (broadcasting); Artificial intelligence; Markov process; Mathematical optimization; Schedule; Computer network; Random variable; Database","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003128771,0.0009506679,0.001788484,0.0005668484,0.0008260753,0.001213774,0.002313062,0.001379139,0.001892364],"category_scores_gemma":[0.008157811,0.0005985373,0.000652656,0.00104964,0.001066878,0.002050815,0.001765586,0.002037245,0.0004298831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001710083,"about_ca_system_score_gemma":0.003518493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007020888,"about_ca_topic_score_gemma":0.006914272,"domain_scores_codex":[0.9986271,0.0003982594,0.00006184664,0.0003457029,0.0002141724,0.0003529567],"domain_scores_gemma":[0.9943687,0.003686227,0.0004910209,0.0004766928,0.0005764494,0.000400875],"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":[0.0002365067,0.0001351014,0.0009736508,0.00004708288,0.00003105501,0.00005444636,0.00005458809,0.9470438,0.0008682806,0.004154265,0.00139964,0.04500168],"study_design_scores_gemma":[0.000008521011,0.00001548016,0.00005519486,0.000001880257,0.000002326555,0.000006838688,0.00000695606,0.997713,0.0002231762,0.001899731,0.00006450016,0.000002572964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04225906,0.0003056152,0.9545577,0.0003842622,0.00006156542,0.00007021035,0.0001180589,0.0008407383,0.001402856],"genre_scores_gemma":[0.8792326,0.0001468824,0.1178384,0.0002861257,0.00006619222,0.0001453614,0.000236502,0.00009435837,0.001953474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007020888,"threshold_uncertainty_score":0.01654673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03477498577339587,"score_gpt":0.2594133022138545,"score_spread":0.2246383164404586,"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."}}