{"id":"W4413017699","doi":"10.1109/jiot.2025.3596467","title":"FEDORA: Federated Ensemble Reinforcement Learning for DAG-Based Task Offloading and Resource Allocation in MEC","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Reinforcement learning; Task (project management); Resource allocation; Resource management (computing); Artificial intelligence; Distributed computing; Computer network","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":[],"consensus_categories":[],"category_scores_codex":[0.000711335,0.0001386884,0.0002189652,0.0003110007,0.00014545,0.0002846772,0.0002647252,0.00006789014,0.000008556531],"category_scores_gemma":[0.000482658,0.0001262854,0.00006593647,0.0001610184,0.00005607448,0.0002946802,0.00005510714,0.0004154504,0.000001327384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009740454,"about_ca_system_score_gemma":0.00006727543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005535431,"about_ca_topic_score_gemma":0.000009011649,"domain_scores_codex":[0.9986753,0.0001268898,0.0005086084,0.0002510208,0.0002000876,0.0002380866],"domain_scores_gemma":[0.9990453,0.000421595,0.0003170276,0.00007638754,0.00008695501,0.00005277798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004145225,0.00005884999,0.0005170851,0.0001217288,0.00002559086,0.00001489288,0.00294489,0.04618217,0.9370778,0.0004420184,0.003221676,0.008978801],"study_design_scores_gemma":[0.0008270663,0.000228742,0.00003856309,0.0006524989,0.000009035878,0.0000304298,0.0001590304,0.3531262,0.6414526,0.0002569949,0.003127067,0.00009182465],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7500142,0.00006343175,0.2469977,0.0009721163,0.0003998131,0.0001721768,3.324187e-7,0.00002743139,0.001352826],"genre_scores_gemma":[0.9958211,0.00000946441,0.0007687379,0.001275414,0.00004079293,0.000006347771,0.000001089598,0.00001136092,0.00206571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.306944,"threshold_uncertainty_score":0.5149767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02235354687579116,"score_gpt":0.2804484149999305,"score_spread":0.2580948681241393,"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."}}