{"id":"W7116906483","doi":"10.1109/jiot.2025.3647560","title":"Delay Optimization in Hierarchical UAV-Aided Federated Learning for Straggling IoT Devices","year":2025,"lang":"","type":"article","venue":"IEEE Internet of Things Journal","topic":"UAV Applications and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Edge device; Edge computing; Queueing theory; Enhanced Data Rates for GSM Evolution; Process (computing); Computation; Server; Resource allocation; Computation offloading","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001002201,0.0003331516,0.0005221525,0.0007770399,0.0002325159,0.0005202997,0.0004128813,0.0003489566,0.0001268641],"category_scores_gemma":[0.0002818256,0.0003666926,0.0002101643,0.0006366363,0.00006242828,0.0005072719,0.00005159545,0.001344359,0.000002808224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003408781,"about_ca_system_score_gemma":0.0001754963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009293258,"about_ca_topic_score_gemma":0.0000319688,"domain_scores_codex":[0.9972844,0.0001246217,0.001542335,0.0003339519,0.0002550583,0.0004595967],"domain_scores_gemma":[0.9983839,0.0003328304,0.000540779,0.0001360886,0.0004893515,0.0001169935],"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":[0.0001784115,0.0001044642,0.001113629,0.0002712939,0.0002128525,0.000005235144,0.001460455,0.9802448,0.001976345,0.0004882064,0.0002180402,0.01372631],"study_design_scores_gemma":[0.001428975,0.0001455677,0.0001881236,0.001694204,0.0001085862,0.00003810034,0.0002830176,0.9886536,0.006137975,0.0004423202,0.0005993791,0.0002801483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1636262,0.0007115519,0.8330603,0.0003004635,0.0009487374,0.0003891877,0.000002539593,0.00005380965,0.0009072084],"genre_scores_gemma":[0.9043999,0.0003909449,0.09431887,0.00009379131,0.0001609211,0.00002586541,0.00001853437,0.0000540829,0.0005370361],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7407737,"threshold_uncertainty_score":0.9998785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01029959843176516,"score_gpt":0.2586598593815733,"score_spread":0.2483602609498081,"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."}}