{"id":"W4281638644","doi":"10.1109/jsac.2022.3180799","title":"Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Telecommunications link; Computer science; Interference (communication); Convergence (economics); Wireless; Transmission (telecommunications); Wireless network; Computer network; Optimization problem; Distributed computing; Channel (broadcasting); 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":"codex-gemma-dda1882f352a","candidate_categories":["sts","open_science","research_integrity"],"consensus_categories":["open_science"],"category_scores_codex":[0.001261785,0.0001718323,0.0001979139,0.0004567748,0.001300992,0.0002468349,0.02988722,0.00006775653,0.00000721619],"category_scores_gemma":[0.001261702,0.0001620455,0.00005286734,0.002169975,0.000078479,0.000300816,0.02431101,0.00267017,0.000002570936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005900163,"about_ca_system_score_gemma":0.0001138377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003875979,"about_ca_topic_score_gemma":0.0003073351,"domain_scores_codex":[0.9976011,0.0008644063,0.000526326,0.0003140836,0.0002686809,0.0004254072],"domain_scores_gemma":[0.9938026,0.001005344,0.0003337665,0.004669746,0.0001406925,0.00004783233],"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":[0.0003973342,0.007198739,0.02474738,0.00007680773,0.0003496364,0.0002026833,0.002464779,0.4842297,0.001702384,0.01260948,0.1673145,0.2987066],"study_design_scores_gemma":[0.001005924,0.0001453093,0.005317895,0.0000918813,0.000005338681,0.0000384162,0.0002286334,0.9854272,0.0001075496,0.003782866,0.003658786,0.0001901947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06437098,0.0007410747,0.9054834,0.02676514,0.000596179,0.0009192988,0.00001235352,0.0004932269,0.0006183634],"genre_scores_gemma":[0.9414189,0.000745521,0.05704639,0.0003567156,0.00001248308,0.000309046,0.00001991933,0.00001818078,0.00007286863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8770479,"threshold_uncertainty_score":0.9999992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05358198947933894,"score_gpt":0.3088880274699237,"score_spread":0.2553060379905847,"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."}}