{"id":"W4417470002","doi":"10.1109/tccn.2025.3645473","title":"Adaptive Layer-Wise Personalized Federated Deep Reinforcement Learning for Heterogeneous Edge Caching","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province","keywords":"Reinforcement learning; Cache; Personalization; Adaptation (eye); Latency (audio); Edge device; Enhanced Data Rates for GSM Evolution; Edge computing","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":[0.0007125285,0.0005316854,0.0005508349,0.0004368859,0.006304251,0.000788276,0.0008155433,0.0002283568,0.00001716396],"category_scores_gemma":[0.00002337811,0.0006147302,0.0003997464,0.0007967597,0.0004108715,0.0003828085,0.00007012671,0.001267745,0.000009320665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002347187,"about_ca_system_score_gemma":0.0002280214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001729124,"about_ca_topic_score_gemma":0.0003624253,"domain_scores_codex":[0.9966496,0.0008340352,0.0007421369,0.0008395496,0.0002586835,0.0006760184],"domain_scores_gemma":[0.9953389,0.002667406,0.0003182287,0.0007913664,0.0007003434,0.0001837624],"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":[0.0007811961,0.0004721831,0.00002794704,0.00008792583,0.001189243,0.000006311857,0.003792035,0.1120962,0.0003377588,0.0009455291,0.00003173371,0.8802319],"study_design_scores_gemma":[0.002513111,0.0006200867,0.000007760428,0.002132562,0.0005418992,0.00001946168,0.001726856,0.988843,0.0005173183,0.0001737275,0.002338894,0.0005653157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002411256,0.01521956,0.9772837,0.0007378464,0.00115541,0.001270149,0.0000177803,0.0001845087,0.00171983],"genre_scores_gemma":[0.9804153,0.01539896,0.000810088,0.001016053,0.00009370858,0.0005037048,0.0000323413,0.00004046514,0.001689379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.978004,"threshold_uncertainty_score":0.9996304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06229990113681563,"score_gpt":0.2965203839878673,"score_spread":0.2342204828510517,"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."}}