{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:01:42Z","timestamp":1776441702734,"version":"3.51.2"},"reference-count":33,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T00:00:00Z","timestamp":1651449600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T00:00:00Z","timestamp":1651449600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100009941","name":"Ministry of Defence","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100009941","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,2]]},"DOI":"10.1109\/infocom48880.2022.9796818","type":"proceedings-article","created":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T21:18:49Z","timestamp":1655759929000},"page":"1449-1458","source":"Crossref","is-referenced-by-count":78,"title":["Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization"],"prefix":"10.1109","author":[{"given":"Jake","family":"Perazzone","sequence":"first","affiliation":[{"name":"Army Research Laboratory,Adelphi,MD,USA"}]},{"given":"Shiqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"IBM T. J. Watson Research Center,Yorktown Heights,NY,USA"}]},{"given":"Mingyue","family":"Ji","sequence":"additional","affiliation":[{"name":"University of Utah,Department of Electrical &#x0026; Computer Engineering,Salt Lake City,UT,USA"}]},{"given":"Kevin S.","family":"Chan","sequence":"additional","affiliation":[{"name":"Army Research Laboratory,Adelphi,MD,USA"}]}],"member":"263","reference":[{"key":"ref33","article-title":"Federated learning with non-iid data","author":"zhao","year":"2018"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref31","article-title":"Leaf: A benchmark for federated settings","author":"caldas","year":"2018"},{"key":"ref30","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009","journal-title":"Tech rep 2 0"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS47774.2020.00026"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref12","first-page":"1709","article-title":"Qsgd: Communication-efficient sgd via gradient quantization and encoding","volume":"30","author":"alistarh","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref13","article-title":"Optimal gradient compression for distributed and federated learning","author":"albasyoni","year":"2020"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref15","doi-asserted-by":"crossref","DOI":"10.52591\/lxai2020071310","article-title":"Communication-efficient federated learning via optimal client sampling","author":"ribero","year":"2020"},{"key":"ref16","article-title":"Active federated learning","author":"goetz","year":"2019"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2019.2944169"},{"key":"ref18","article-title":"Client selection in federated learning: Convergence analysis and power-of-choice selection strategies","author":"cho","year":"2020"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3015671"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2984332"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.10.007"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3040887"},{"key":"ref3","article-title":"Achieving linear convergence in federated learning under objective and systems heterogeneity","author":"mitra","year":"2021"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3338501.3357370"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.2200\/S00271ED1V01Y201006CNT007"},{"key":"ref5","article-title":"Differentially private federated learning: A client level perspective","author":"geyer","year":"2017"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref7","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"li","year":"2020","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"ref2","article-title":"On the convergence of FedAvg on non-iid data","author":"li","year":"2019"},{"key":"ref9","article-title":"Federated learning: Strategies for improving communication efficiency","author":"kone?n?","year":"2016"},{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref20","first-page":"3403","article-title":"Towards flexible device participation in federated learning","author":"ruan","year":"2021","journal-title":"International Conference on Artificial Intelligence and Statistics"},{"key":"ref22","article-title":"Achieving linear speedup with partial worker participation in non-iid federated learning","author":"yang","year":"2021"},{"key":"ref21","first-page":"5132","article-title":"Scaffold: Stochastic controlled averaging for federated learning","author":"karimireddy","year":"2020","journal-title":"International Conference on Machine Learning"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3024629"},{"key":"ref23","article-title":"Fast federated learning in the presence of arbitrary device unavailability","author":"gu","year":"2021"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC45663.2020.9120649"},{"key":"ref25","article-title":"Energy efficient federated learning over wireless communication networks","author":"yang","year":"2020","journal-title":"IEEE Transactions on Wireless Communications"}],"event":{"name":"IEEE INFOCOM 2022 - IEEE Conference on Computer Communications","location":"London, United Kingdom","start":{"date-parts":[[2022,5,2]]},"end":{"date-parts":[[2022,5,5]]}},"container-title":["IEEE INFOCOM 2022 - IEEE Conference on Computer Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9796607\/9796652\/09796818.pdf?arnumber=9796818","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T21:40:38Z","timestamp":1675892438000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9796818\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,2]]},"references-count":33,"URL":"https:\/\/doi.org\/10.1109\/infocom48880.2022.9796818","relation":{},"subject":[],"published":{"date-parts":[[2022,5,2]]}}}