{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T19:22:08Z","timestamp":1794079328531,"version":"4.8.0"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100003816","name":"Huawei Technologies","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003816","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2022,2,1]]},"DOI":"10.1109\/jiot.2021.3096570","type":"journal-article","created":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T20:52:38Z","timestamp":1626123158000},"page":"1640-1654","source":"Crossref","is-referenced-by-count":34,"title":["Data and Channel-Adaptive Sensor Scheduling for Federated Edge Learning via Over-the-Air Gradient Aggregation"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0025-7741","authenticated-orcid":false,"given":"Liqun","family":"Su","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7769-6008","authenticated-orcid":false,"given":"Vincent K. N.","family":"Lau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2896243"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2903832"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.1900103"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2015.2502182"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2019.2908403"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2008.924635"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2007.911284"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2876097"},{"key":"ref9","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Stat.","author":"McMahan"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2018.1429274"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3278721.3278778"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2951687"},{"key":"ref13","volume-title":"An overview of gradient descent optimization algorithms","author":"Ruder","year":"2016"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"ref15","first-page":"472","article-title":"A loss function analysis for classification methods in text categorization","volume-title":"Proc. 20th Int. Conf. Mach. Learn. (ICML)","author":"Li"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2272921"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2017.2686002"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2843321"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM38437.2019.9013170"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2018.2871070"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2019.2934956"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2019.2961673"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2019.2946245"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.2974748"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/SPAWC.2019.8815498"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2020.2999606"},{"key":"ref27","first-page":"45","article-title":"Support vector machine active learning with applications to text classification","volume":"2","author":"Tong","year":"2001","journal-title":"J. Mach. Learn. Res."},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2008.4563068"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT44484.2020.9173960"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3015671"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.2200\/s00271ed1v01y201006cnt007"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2008.2008232"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.2980914"},{"key":"ref34","first-page":"4447","article-title":"Sparsified SGD with memory","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Stich"},{"key":"ref35","first-page":"11446","article-title":"Communication-efficient distributed blockwise momentum sgd with error-feedback","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zheng"},{"key":"ref36","volume-title":"On large-batch training for deep learning: Generalization gap and sharp minima","author":"Keskar","year":"2016"},{"issue":"1","key":"ref37","first-page":"5","article-title":"Support vector machines for classification and regression","volume-title":"ISIS Tech. Rep.","volume":"14","author":"Gunn","year":"1998"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2011.2169407"},{"key":"ref39","volume-title":"A Lyapunov analysis for accelerated gradient methods: From deterministic to stochastic case","author":"Laborde","year":"2019"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.2981904"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref42","first-page":"123","article-title":"Convex neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bengio"},{"issue":"1","key":"ref43","first-page":"629","article-title":"Breaking the curse of dimensionality with convex neural networks","volume":"18","author":"Bach","year":"2017","journal-title":"J. Mach. Learn. Res."},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1137\/16M1080173"},{"key":"ref45","volume-title":"Cooperative SGD: A unified framework for the design and analysis of communication-efficient SGD algorithms","author":"Wang","year":"2018"},{"key":"ref46","volume-title":"The MNIST Database of Handwritten Digits","author":"LeCun","year":"1998"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2017.01.083"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6488907\/9690636\/09481289.pdf?arnumber=9481289","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:04:37Z","timestamp":1705017877000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9481289\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,1]]},"references-count":47,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2021.3096570","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,1]]}}}