{"id":"W4388040460","doi":"10.1109/pimrc56721.2023.10293758","title":"Latency Minimization in Wireless-Powered Federated Learning Networks with NOMA","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Université Laval","funders":"","keywords":"Noma; Computer science; Telecommunications link; Wireless; Latency (audio); Wireless network; Minification; Convex optimization; Optimization problem; Distributed computing; Computer network; Mathematical optimization; Regular polygon; Algorithm; Telecommunications","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":[0.001393185,0.0006915557,0.0007237311,0.0003311189,0.0005308006,0.0009429043,0.0008692949,0.0007462821,0.0008748078],"category_scores_gemma":[0.002517827,0.000245395,0.0003271941,0.0005985838,0.0006634652,0.001229791,0.001115679,0.0006343585,0.0001302254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008951456,"about_ca_system_score_gemma":0.0009318654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002024145,"about_ca_topic_score_gemma":0.001943769,"domain_scores_codex":[0.9993392,0.0002784944,0.00002172343,0.00009317449,0.0001203122,0.0001471959],"domain_scores_gemma":[0.9988536,0.00069989,0.0001538252,0.00006707953,0.0001614347,0.0000641587],"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":[0.0001133461,0.00005629504,0.0003941695,0.00004931474,0.00001947052,0.00008225966,0.0000429147,0.9636281,0.001229084,0.009228596,0.000539861,0.02461662],"study_design_scores_gemma":[0.000004953742,0.00002941944,0.0000503116,0.000002641734,0.000002642348,0.00001580218,0.00001497321,0.9958631,0.0002819202,0.003619495,0.0001117652,0.000002891962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09963576,0.0007800911,0.8955188,0.0003613381,0.00005771303,0.00004045417,0.00004901523,0.0002165708,0.003340165],"genre_scores_gemma":[0.974515,0.0001728968,0.02369141,0.00007544865,0.0000177353,0.00003859001,0.00002215087,0.00001375186,0.001452953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002024145,"threshold_uncertainty_score":0.007367969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009064823013887684,"score_gpt":0.2107137568448829,"score_spread":0.2016489338309952,"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."}}