{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T16:25:57Z","timestamp":1783614357605,"version":"3.55.0"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2148224"],"award-info":[{"award-number":["2148224"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"name":"OUSD R&amp;E"},{"DOI":"10.13039\/100000161","name":"National Institute of Standards and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000161","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Industry Partners as specified in the Resilient &amp; Intelligent NextG Systems"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Signal Process."],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1109\/jstsp.2022.3224590","type":"journal-article","created":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T21:59:39Z","timestamp":1669327179000},"page":"98-111","source":"Crossref","is-referenced-by-count":54,"title":["Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints"],"prefix":"10.1109","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9634-0880","authenticated-orcid":false,"given":"Monica","family":"Ribero","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7945-4114","authenticated-orcid":false,"given":"Haris","family":"Vikalo","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1498-494X","authenticated-orcid":false,"given":"Gustavo","family":"de Veciana","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"374","article-title":"Towards federated learning at scale: System design","volume-title":"Proc. Mach. Learn. Syst.","author":"Bonawitz","year":"2019"},{"key":"ref2","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Simonyan","year":"2015"},{"key":"ref3","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","author":"McMahan","year":"2017"},{"key":"ref4","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy","year":"2020"},{"key":"ref5","article-title":"Adaptive federated optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Reddi","year":"2020"},{"key":"ref6","article-title":"Federated learning based on dynamic regularization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Acar","year":"2021"},{"key":"ref7","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","author":"Li","year":"2018"},{"key":"ref8","first-page":"1764","article-title":"Semi-cyclic stochastic gradient descent","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Eichner","year":"2019"},{"key":"ref9","article-title":"A field guide to federated optimization","author":"Wang","year":"2021"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref11","article-title":"Client selection in federated learning: Convergence analysis and power-of-choice selection strategies","author":"Cho","year":"2020"},{"key":"ref12","article-title":"On the convergence of FedAvg on non-IID data","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li","year":"2020"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.52591\/lxai2020071310"},{"key":"ref14","first-page":"1000","article-title":"Communication-efficient distributed optimization using an approximate Newton-type method","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Shamir","year":"2014"},{"key":"ref15","first-page":"4424","article-title":"Federated multi-task learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Smith","year":"2017"},{"key":"ref16","first-page":"6155","article-title":"DoubleSqueeze: Parallel stochastic gradient descent with double-pass error-compensated compression","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tang","year":"2019"},{"key":"ref17","article-title":"Federated learning: Strategies for improving communication efficiency","volume-title":"Proc. Workshop Private Multi-Party Mach. Learn.","author":"Konen","year":"2016"},{"key":"ref18","first-page":"3329","article-title":"Distributed mean estimation with limited communication","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Suresh","year":"2017"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3389\/fams.2018.00062"},{"key":"ref20","first-page":"1709","article-title":"QSGD: Communication-efficient SGD via gradient quantization and encoding","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Alistarh","year":"2017"},{"key":"ref21","first-page":"129","article-title":"Natural compression for distributed deep learning","volume-title":"Proc. Math. Sci. Mach. Learn.","author":"Horvath","year":"2022"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3008091"},{"key":"ref23","article-title":"Federated learning for mobile keyboard prediction","author":"Hard","year":"2018"},{"key":"ref24","article-title":"Applied federated learning: Improving Google Keyboard query suggestions","author":"Yang","year":"2018"},{"key":"ref25","first-page":"629","article-title":"GAIA: Geo-distributed machine learning approaching {LAN} speeds","volume-title":"Proc. 14th USENIX Symp. Networked Syst. Des. Implementation","author":"Hsieh","year":"2017"},{"key":"ref26","first-page":"5050","article-title":"LAG: Lazily aggregated gradient for communication-efficient distributed learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen","year":"2018"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CDC42340.2020.9303828"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3210365"},{"key":"ref29","first-page":"1","article-title":"Stochastic optimization with importance sampling for regularized loss minimization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhao","year":"2015"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378161"},{"key":"ref31","first-page":"3581","article-title":"Federated learning with buffered asynchronous aggregation","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Nguyen","year":"2021"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1137\/090757125"},{"key":"ref33","first-page":"4615","article-title":"Agnostic federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Mohri","year":"2019"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2012.6426691"},{"key":"ref35","article-title":"Local SGD converges fast and communicates little","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Stich","year":"2019"},{"key":"ref36","first-page":"4452","article-title":"Sparsified SGD with memory","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Stich","year":"2018"},{"key":"ref37","first-page":"9324","article-title":"Quantized decentralized stochastic learning over directed graphs","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Taheri"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1090\/mbk\/107"},{"key":"ref39","first-page":"4387","article-title":"The non-IID data quagmire of decentralized machine learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hsieh"},{"key":"ref40","article-title":"TensorFlow federated","year":"2019"},{"key":"ref41","first-page":"20461","article-title":"On large-cohort training for federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Charles","year":"2021"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2002.1019359"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1287\/opre.1040.0156"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316962"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01261-8_1"}],"container-title":["IEEE Journal of Selected Topics in Signal Processing"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/4200690\/10050192\/9963723-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4200690\/10050192\/09963723.pdf?arnumber=9963723","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T03:10:25Z","timestamp":1706757025000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9963723\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":46,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/jstsp.2022.3224590","relation":{},"ISSN":["1932-4553","1941-0484"],"issn-type":[{"value":"1932-4553","type":"print"},{"value":"1941-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]}}}