{"id":"W4400681763","doi":"10.1109/tcomm.2024.3429170","title":"Federated Unfolding Learning for CSI Feedback in Distributed Edge Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Distributed learning; Distributed computing; Electronic engineering; Artificial intelligence; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0002297067,0.0001598043,0.000153867,0.0001744089,0.0008817051,0.0004714456,0.0009901311,0.00009497157,0.000009871176],"category_scores_gemma":[0.000006195793,0.0001658379,0.0001242308,0.001498585,0.00006502485,0.0003816644,0.00001489111,0.000666127,0.00003480198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001122648,"about_ca_system_score_gemma":0.00006539582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003411319,"about_ca_topic_score_gemma":0.0002030558,"domain_scores_codex":[0.9988026,0.0001123151,0.0003319872,0.0003509045,0.00009901002,0.0003032067],"domain_scores_gemma":[0.9980411,0.0009095651,0.00004521178,0.0008443931,0.00007806859,0.00008167065],"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":[0.00001245363,0.000381268,0.00001611591,0.00002915351,0.0000691115,0.0000026762,0.0003025373,0.7350203,0.0009172854,0.03700958,0.002417312,0.2238222],"study_design_scores_gemma":[0.0001933937,0.00003885248,0.00006014663,0.0001016682,0.00001422722,0.000006330141,0.00003499008,0.970805,0.0004872886,0.0008629702,0.02722001,0.0001751077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005097487,0.0005475261,0.990934,0.006182523,0.0003522604,0.0004607378,0.00002294841,0.0005468297,0.0004434087],"genre_scores_gemma":[0.9906826,0.0005440743,0.007498676,0.0001227114,0.00003671823,0.0006185981,0.00005245085,0.00002291592,0.000421278],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9901728,"threshold_uncertainty_score":0.6781452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03533109269628402,"score_gpt":0.292490020897497,"score_spread":0.257158928201213,"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."}}