{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:07:07Z","timestamp":1770739627150,"version":"3.49.0"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T00:00:00Z","timestamp":1771113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T00:00:00Z","timestamp":1771113600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T00:00:00Z","timestamp":1771113600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2025ZD1605600"],"award-info":[{"award-number":["2025ZD1605600"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62303212"],"award-info":[{"award-number":["62303212"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306289"],"award-info":[{"award-number":["62306289"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2024A1515011633"],"award-info":[{"award-number":["2024A1515011633"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2024B1212010002"],"award-info":[{"award-number":["2024B1212010002"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2026,2,15]]},"DOI":"10.1109\/jiot.2025.3637373","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T19:05:20Z","timestamp":1764183920000},"page":"6437-6452","source":"Crossref","is-referenced-by-count":0,"title":["SET-LASQ: A Temporal Information-Based Stochastic Event-Triggered Method for Communication-Efficient Federated Learning"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1078-4514","authenticated-orcid":false,"given":"Xiaohong","family":"Li","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2707-8751","authenticated-orcid":false,"given":"Jun","family":"Sun","sequence":"additional","affiliation":[{"name":"Zhejiang Laboratory, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8288-3833","authenticated-orcid":false,"given":"Zaiyue","family":"Yang","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2534-8543","authenticated-orcid":false,"given":"Kemi","family":"Ding","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Federated learning: Strategies for improving communication efficiency","author":"Kone\u010dn\u00fd","year":"2016","journal-title":"arXiv:1610.05492"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3145865"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3033286"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3324079"},{"key":"ref6","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","author":"Han","year":"2015","journal-title":"arXiv:1510.00149"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3041185"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3294295"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2005.843546"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2011.2171686"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2014-274"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3385913"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3073112"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3213650"},{"key":"ref16","first-page":"5325","article-title":"Error compensated quantized SGD and its applications to large-scale distributed optimization","volume-title":"Proc. 35th Int. Conf. Mach. Learn. (ICML)","volume":"80","author":"Wu"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2023.3244092"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3345367"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICTC55196.2022.9952431"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1045"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2022.3145576"},{"key":"ref22","first-page":"4447","article-title":"Sparsified SGD with memory","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Stich"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.3042094"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1080\/10556788.2022.2117355"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3343288"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT57864.2024.10619383"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3499375"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3198176"},{"key":"ref29","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","volume":"54","author":"McMahan"},{"key":"ref30","first-page":"1000","article-title":"Communication-efficient distributed optimization using an approximate Newton-type method","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Shamir"},{"key":"ref31","first-page":"362","article-title":"Disco: Distributed optimization for self-concordant empirical loss","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","volume":"37","author":"Zhang"},{"key":"ref32","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Tian","year":"2018","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1080\/0952813X.2022.2079730"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3438843"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3368473"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2024.3408631"},{"key":"ref37","article-title":"LAG: Lazily aggregated gradient for communication-efficient distributed learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3333804"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2023.3295734"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2021.3099977"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2024.3368751"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2023.111486"},{"key":"ref43","article-title":"Communication-efficient distributed learning via lazily aggregated quantized gradients","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sun"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2024789118"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3490855"},{"key":"ref46","article-title":"QSGD: Communication-efficient SGD via gradient quantization and encoding","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Alistarh"},{"key":"ref47","article-title":"Gradient sparsification for communication-efficient distributed optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wangni"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/11372629\/11269832.pdf?arnumber=11269832","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T21:08:47Z","timestamp":1770671327000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11269832\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,15]]},"references-count":47,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2025.3637373","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,15]]}}}