{"id":"W4323645885","doi":"10.1109/fnwf55208.2022.00050","title":"Cost-efficient Federated Reinforcement Learning- Based Network Routing for Wireless Networks","year":2022,"lang":"en","type":"article","venue":"","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Reinforcement learning; Computer science; Distributed computing; Robustness (evolution); Routing protocol; Routing domain; Routing (electronic design automation); Static routing; Computer network; Artificial intelligence","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001548179,0.0002536892,0.0002936161,0.00005914428,0.0022512,0.0004465114,0.000877983,0.00006837615,0.0002164957],"category_scores_gemma":[0.00001760504,0.000246074,0.0001568538,0.0008256892,0.00002477924,0.0001054559,0.0006681331,0.0004693232,0.000005973615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001896598,"about_ca_system_score_gemma":0.0001620079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003254939,"about_ca_topic_score_gemma":0.00000719963,"domain_scores_codex":[0.9972466,0.0002621412,0.0004850593,0.0006013563,0.0004633662,0.0009414292],"domain_scores_gemma":[0.9987016,0.0004045669,0.0002618126,0.0003487097,0.0001249145,0.0001583427],"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":[0.0000609864,0.00004859366,0.0001595019,0.000007860669,0.00001602937,0.000005111313,0.00006230686,0.9520857,0.000005408589,0.01920499,0.008321999,0.02002147],"study_design_scores_gemma":[0.001054223,0.0003857613,0.00004823399,0.0000226868,0.000005398721,0.000003617414,0.0000398156,0.9439664,0.00005084601,0.00005933233,0.05403714,0.0003265559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003148875,0.00003593275,0.9875493,0.0005693521,0.000537706,0.009013016,8.7266e-7,0.0004670504,0.001511905],"genre_scores_gemma":[0.9701921,0.000001437146,0.007696584,0.002024688,0.000355594,0.01822097,0.00004222285,0.00003307616,0.001433371],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9798527,"threshold_uncertainty_score":0.9999992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03172114298392512,"score_gpt":0.2730078217337095,"score_spread":0.2412866787497844,"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."}}