{"id":"W3113024443","doi":"10.1109/jsac.2020.3036946","title":"Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching","year":2020,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":150,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; Academy of Finland; Natural Sciences and Engineering Research Council of Canada; Chongqing Research Program of Basic Research and Frontier Technology; Shenzhen University","keywords":"Computer science; Reinforcement learning; Markov decision process; Edge device; Cache; Enhanced Data Rates for GSM Evolution; Distributed computing; Base station; Computer network; Node (physics); Cloud computing; Integer programming; Mobile edge computing; Quality of service; Server; Markov process; Artificial intelligence","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":[0.001022026,0.0007907267,0.001121851,0.0002675267,0.000314533,0.0006330681,0.00159814,0.001169916,0.00125327],"category_scores_gemma":[0.002704994,0.0003471395,0.0003978793,0.0003732104,0.0007842134,0.0009174559,0.0009412138,0.001310678,0.0001866661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001068007,"about_ca_system_score_gemma":0.001119941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0106793,"about_ca_topic_score_gemma":0.009853029,"domain_scores_codex":[0.9996094,0.00009868513,0.00001859695,0.0001031311,0.00006917338,0.0001011097],"domain_scores_gemma":[0.9989145,0.0006037984,0.0001142815,0.00007315325,0.0002190239,0.00007530212],"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":[0.00008628682,0.00009628322,0.0009100446,0.00003223052,0.0000290722,0.00008392047,0.00003557031,0.9671172,0.001103333,0.003365194,0.0008039629,0.02633695],"study_design_scores_gemma":[0.000003750919,0.00001077376,0.00003128201,0.000001164847,0.000002635766,0.000003347182,0.000001498013,0.9991684,0.00009239271,0.0006424344,0.00004104365,0.000001300793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07016665,0.0004699258,0.9261397,0.0003244028,0.00007042768,0.00004400737,0.00006023069,0.0005609908,0.002163653],"genre_scores_gemma":[0.9739447,0.000095499,0.02385747,0.0001540944,0.00001989159,0.00005499738,0.00006022921,0.00002077753,0.001792368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0106793,"threshold_uncertainty_score":0.02123427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05197815145586125,"score_gpt":0.302713714603654,"score_spread":0.2507355631477927,"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."}}