{"id":"W4406949338","doi":"10.1109/tcomm.2025.3535878","title":"Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"King Abdullah University of Science and Technology","keywords":"Computer science; Human–computer interaction; Online learning; Multimedia","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008701975,0.0006257897,0.0009337039,0.0003879956,0.0006087117,0.00148923,0.002071174,0.001186492,0.001609736],"category_scores_gemma":[0.004230605,0.0002571582,0.0004406936,0.0008915735,0.0005747147,0.002088384,0.001276565,0.0009685055,0.0004175225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001455816,"about_ca_system_score_gemma":0.001129466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006245981,"about_ca_topic_score_gemma":0.00748499,"domain_scores_codex":[0.9992705,0.0002110025,0.00004021972,0.0001795079,0.0001715219,0.0001272312],"domain_scores_gemma":[0.9979481,0.0008118774,0.0001501148,0.0005865478,0.0003971545,0.0001062419],"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":[0.0003733732,0.0002555638,0.001707644,0.0001338561,0.00006995792,0.0002230991,0.0001657483,0.6712298,0.009865723,0.04887104,0.005084858,0.2620192],"study_design_scores_gemma":[0.000006830529,0.00002847021,0.0001193471,0.000004862727,0.000006709066,0.00004241939,0.00001482757,0.9907651,0.001231412,0.007098507,0.0006753647,0.000006173223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02783956,0.0005511708,0.9684284,0.0001999297,0.00004481288,0.00003930546,0.00005542768,0.0007344806,0.002106953],"genre_scores_gemma":[0.8606853,0.0004601408,0.135825,0.0001403306,0.00005344212,0.00007054865,0.0001438493,0.00006937215,0.00255188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006245981,"threshold_uncertainty_score":0.01241922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02282307567392406,"score_gpt":0.2909144032025605,"score_spread":0.2680913275286365,"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."}}