{"id":"W3199147997","doi":"10.1109/mnet.002.2000334","title":"UAV-Assisted Communication Efficient Federated Learning in the Era of the Artificial Intelligence of Things","year":2021,"lang":"en","type":"article","venue":"IEEE Network","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Incentive; Cloud computing; Incentive compatibility; Relay; Distributed computing; Service provider; Internet of Things; Computer network; Artificial intelligence; Computer security; Service (business)","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":[0.001261526,0.0003091054,0.0005146573,0.0001961458,0.0005073391,0.0007053411,0.0007162931,0.0005534624,0.0006819804],"category_scores_gemma":[0.001964224,0.0001373408,0.0003471919,0.0004051848,0.0005492951,0.00151243,0.001342808,0.0007720959,0.00009620684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005819055,"about_ca_system_score_gemma":0.000807467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002145682,"about_ca_topic_score_gemma":0.001482939,"domain_scores_codex":[0.9993654,0.0002663086,0.00002342545,0.00009975217,0.000136034,0.0001090549],"domain_scores_gemma":[0.999082,0.0004463598,0.000120208,0.0001916634,0.0001041947,0.00005556926],"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":[0.0001749924,0.00006926832,0.001035904,0.00005699813,0.00002852717,0.0002110171,0.0001092856,0.8983792,0.003603058,0.04233963,0.001481013,0.05251106],"study_design_scores_gemma":[0.000005330805,0.00003023836,0.00009307262,0.000003444358,0.000003615651,0.00003470587,0.00001764902,0.987233,0.0007327434,0.0111879,0.0006545841,0.000003694521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05497764,0.0003697873,0.9411237,0.0004452021,0.00005441135,0.00002734477,0.00004216967,0.0002215652,0.002738157],"genre_scores_gemma":[0.9684406,0.0001635494,0.03025277,0.00009881846,0.00001364132,0.00002751214,0.00003383077,0.00001097114,0.0009582725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002145682,"threshold_uncertainty_score":0.006671727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04558183010341097,"score_gpt":0.2824922611905125,"score_spread":0.2369104310871015,"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."}}