{"id":"W4390871644","doi":"10.1109/twc.2023.3334691","title":"OFDMA-F²L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Context (archaeology); Wireless; Limiting; Perceptron; Integer (computer science); Upper and lower bounds; Orthogonal frequency-division multiple access; Channel (broadcasting); Mathematical optimization; Algorithm; Artificial intelligence; Orthogonal frequency-division multiplexing; Artificial neural network; Computer network; Mathematics; Telecommunications","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":[0.00242111,0.0006719115,0.0008781512,0.0004100548,0.0007542849,0.001446112,0.001639488,0.0009502578,0.001180617],"category_scores_gemma":[0.004880607,0.0002453466,0.0004453748,0.0006679275,0.0009162159,0.00188586,0.001982524,0.00150822,0.0003538158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001052221,"about_ca_system_score_gemma":0.001619465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004033119,"about_ca_topic_score_gemma":0.00402295,"domain_scores_codex":[0.9986803,0.0003723734,0.00007556197,0.0002670005,0.0003041023,0.0003006156],"domain_scores_gemma":[0.9977422,0.001000256,0.0002147415,0.0004827605,0.0003942621,0.0001658553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004570044,0.0003481859,0.002242215,0.00007291937,0.00007330775,0.000154757,0.0001517355,0.7501974,0.004864696,0.02727285,0.002772297,0.2113926],"study_design_scores_gemma":[0.000007358091,0.00004253506,0.00007855572,0.000002855106,0.000004101547,0.00001821319,0.00001098449,0.9945351,0.0008312548,0.004097511,0.0003675169,0.000004028281],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05594404,0.0002690307,0.938864,0.0003302631,0.00006916848,0.00006268392,0.00004829281,0.001182456,0.003230063],"genre_scores_gemma":[0.8667514,0.0001313998,0.1308924,0.0001732487,0.0000480318,0.00006941347,0.00009994891,0.0000379546,0.001796091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004033119,"threshold_uncertainty_score":0.01280421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03794358041270843,"score_gpt":0.3079354508574405,"score_spread":0.269991870444732,"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."}}