{"id":"W4288391351","doi":"10.1109/jiot.2022.3194546","title":"Energy-Efficient Resource Allocation for Federated Learning in NOMA-Enabled and Relay-Assisted Internet of Things Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Noma; Computer science; Relay; Resource allocation; Computer network; The Internet; Resource management (computing); Resource (disambiguation); Internet of Things; Distributed computing; Telecommunications link; World Wide Web; Power (physics)","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":[0.001337776,0.0008654997,0.001213905,0.000460762,0.0008146596,0.001027354,0.001750185,0.0008194653,0.001124348],"category_scores_gemma":[0.003465004,0.0003298435,0.0006023251,0.0007993581,0.0006993888,0.001492016,0.001409881,0.0008268236,0.0002006541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008547796,"about_ca_system_score_gemma":0.001102931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002572851,"about_ca_topic_score_gemma":0.003779731,"domain_scores_codex":[0.9990838,0.0003461357,0.00004530066,0.0002175795,0.0001323759,0.0001749564],"domain_scores_gemma":[0.9986066,0.0007899849,0.000154293,0.0002125109,0.0001562464,0.0000803507],"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":[0.0001677887,0.00009116406,0.000491553,0.00006843264,0.00003798323,0.0001342178,0.00006962175,0.9401983,0.001996182,0.01041468,0.001373749,0.04495635],"study_design_scores_gemma":[0.000004509437,0.00002021745,0.00006048026,0.000002907735,0.000005435209,0.00002557305,0.00001286268,0.9952368,0.0003641438,0.004121053,0.0001420471,0.000003953712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05443599,0.0004655948,0.9417495,0.0003067326,0.00007891357,0.00005159612,0.00008278286,0.000523486,0.0023055],"genre_scores_gemma":[0.9555055,0.0001358272,0.04320845,0.0001061573,0.00002248476,0.00006957067,0.00005947891,0.00002396812,0.0008686429],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002572851,"threshold_uncertainty_score":0.007074952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01922197212112504,"score_gpt":0.243821859798605,"score_spread":0.22459988767748,"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."}}