{"id":"W3115760506","doi":"10.1109/tii.2020.3046028","title":"When Information Freshness Meets Service Latency in Federated Learning: A Task-Aware Incentive Scheme for Smart Industries","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Age of Information Optimization","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of British Columbia","funders":"Ministry of Education, India; National Research Foundation Singapore; Israel Science Foundation; Nanyang Technological University; Ministry of Education - Singapore; National Research Foundation","keywords":"Computer science; Incentive; Latency (audio); Incentive compatibility; Task (project management); Flexibility (engineering); Service (business); Scheme (mathematics); Engineering","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"],"consensus_categories":[],"category_scores_codex":[0.000396865,0.0003433901,0.0003669993,0.0004750939,0.0004843091,0.0007408432,0.0006787388,0.0005319173,0.00005527358],"category_scores_gemma":[0.0001713853,0.0003596154,0.00008742401,0.001848727,0.00004373698,0.009301973,0.00001878619,0.001018015,0.0001907857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002452231,"about_ca_system_score_gemma":0.0005494242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008784595,"about_ca_topic_score_gemma":0.00003343062,"domain_scores_codex":[0.9972877,0.00008748371,0.001456906,0.0001764038,0.0005481825,0.0004433387],"domain_scores_gemma":[0.9979476,0.0001937014,0.0006553415,0.0002646066,0.0007143003,0.0002244587],"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":[0.0005540274,0.0001955773,0.0001423685,0.0002732481,0.0001405357,0.000002277603,0.05568799,0.8590131,0.00003329864,0.001186107,0.008680127,0.07409132],"study_design_scores_gemma":[0.003591129,0.0003241733,0.00001282235,0.0001216898,0.00001756974,0.000003380802,0.003336356,0.9731821,0.003975717,0.000046821,0.01495353,0.0004346835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007002531,0.000002082229,0.9845547,0.005278417,0.0006277062,0.001328706,0.00008190766,0.000416421,0.0007075067],"genre_scores_gemma":[0.9636804,0.00002060475,0.03035327,0.005015004,0.0001207458,0.0003519715,0.0003126563,0.00003248623,0.0001128395],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9566779,"threshold_uncertainty_score":0.9998856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04652460839753812,"score_gpt":0.2402356073179888,"score_spread":0.1937109989204507,"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."}}