{"id":"W4280588735","doi":"10.1109/wcnc51071.2022.9771700","title":"Joint Selection of Local Trainers and Resource Allocation for Federated Learning in Open RAN Intelligent Controllers","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Wireless Communications and Networking Conference (WCNC)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Mitacs","keywords":"Computer science; Resource allocation; Software deployment; Selection (genetic algorithm); Radio access network; Convergence (economics); C-RAN; Distributed computing; Ran; Resource management (computing); Resource (disambiguation); Decomposition; Artificial intelligence; Machine learning; Mathematical optimization; Computer network; Base station","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001538552,0.0001457139,0.0003123594,0.000204846,0.0008539075,0.0002425052,0.007119439,0.00007040252,0.000006211981],"category_scores_gemma":[0.000264807,0.0001649436,0.00003024382,0.0006958924,0.0002372761,0.0002612301,0.01935274,0.0005666682,1.321102e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001291192,"about_ca_system_score_gemma":0.0001356259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002679977,"about_ca_topic_score_gemma":0.0004627856,"domain_scores_codex":[0.9982159,0.0005067732,0.0004420688,0.0004133757,0.0001660417,0.0002558059],"domain_scores_gemma":[0.9971223,0.0005411572,0.0002944174,0.001903356,0.00009419817,0.00004459498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000124802,0.000182545,0.00107422,0.00004127148,0.00007541946,0.000001302136,0.001962846,0.00437343,0.003499697,0.01651199,0.00295692,0.9691955],"study_design_scores_gemma":[0.0007631893,0.0001828268,0.0001530217,0.00007877243,0.000009235343,0.000009516097,0.002922499,0.9837503,0.0004489813,0.004383512,0.007123524,0.000174583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06803021,0.001203425,0.9124779,0.01618123,0.0001612135,0.001257035,0.0000136858,0.0002260273,0.0004492727],"genre_scores_gemma":[0.9840454,0.001486332,0.0138487,0.00009503937,0.00001025992,0.0003950045,0.0000691819,0.00001296,0.00003712846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9793769,"threshold_uncertainty_score":0.9982525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08203369062551207,"score_gpt":0.2998206270219665,"score_spread":0.2177869363964544,"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."}}