{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T05:01:13Z","timestamp":1744434073361,"version":"3.37.3"},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council (ERC)","doi-asserted-by":"publisher","award":["677854"],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"UK EPSRC","doi-asserted-by":"publisher","award":["EP\/T02360011"],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,12]]},"DOI":"10.1109\/isit45174.2021.9517850","type":"proceedings-article","created":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T20:52:42Z","timestamp":1630529562000},"page":"467-472","source":"Crossref","is-referenced-by-count":22,"title":["FedADC: Accelerated Federated Learning with Drift Control"],"prefix":"10.1109","author":[{"given":"Emre","family":"Ozfatura","sequence":"first","affiliation":[]},{"given":"Kerem","family":"Ozfatura","sequence":"additional","affiliation":[]},{"given":"Deniz","family":"Gunduz","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_5"},{"key":"ref11","first-page":"4387","article-title":"The non-IID data quagmire of decentralized machine learning","volume":"119","author":"hsieh","year":"2020","journal-title":"Proceedings of the 37th International Conference on Machine Learning ser Proceedings of Machine Learning Research"},{"key":"ref12","article-title":"Federated learning with non-iid data","volume":"abs 1806 582","author":"zhao","year":"2018","journal-title":"CoRR"},{"key":"ref13","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume":"119","author":"karimireddy","year":"2020","journal-title":"Proceedings of the 37th International Conference on Machine Learning ser Proceedings of Machine Learning Research"},{"key":"ref14","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"li","year":"0","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"ref15","first-page":"314","article-title":"Stochastic variance reduction for nonconvex optimization","volume":"48","author":"reddi","year":"2016","journal-title":"ser Proceedings of Machine Learning Research"},{"key":"ref16","first-page":"543","article-title":"A method for solving the convex programming problem with convergence rate (1\/k2)","volume":"269","author":"nesterov","year":"0","journal-title":"Proceedings of the USSR Academy of Sciences"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/0041-5553(64)90137-5"},{"key":"ref18","first-page":"1139","article-title":"On the importance of initialization and momentum in deep learning","volume":"28","author":"sutskever","year":"2013","journal-title":"ser Proceedings of Machine Learning Research"},{"key":"ref19","article-title":"SLOWMO: Improving communication-efficient distributed SGD with slow momentum","author":"wang","year":"0","journal-title":"International Conference on Learning Representations"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32692-0_16"},{"key":"ref3","article-title":"Federated learning for emoji prediction in a mobile keyboard","volume":"abs 1906 4329","author":"ramaswamy","year":"2019","journal-title":"CoRR"},{"key":"ref6","article-title":"Dopamine: Differentially private secure federated learning on medical data","author":"malekzadeh","year":"2021","journal-title":"Proceedings of the Second AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI-21) Virtual Worskhop"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00323-1"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.2000394"},{"key":"ref7","article-title":"Wireless network intelligence at the edge","volume":"abs 1812 2858","author":"park","year":"2018","journal-title":"CoRR"},{"key":"ref2","article-title":"Federated learning for mobile keyboard prediction","volume":"abs 1811 3604","author":"hard","year":"2018","journal-title":"CoRR"},{"key":"ref1","first-page":"1273","article-title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","volume":"54","author":"mcmahan","year":"2017","journal-title":"Proceedings of the 20th international Conference on Artificial Intelligence and Statistics ser Proceedings of Machine Learning Research"},{"key":"ref9","article-title":"Measuring the effects of nonidentical data distribution for federated visual classification","volume":"abs 1909 6335","author":"hsu","year":"2019","journal-title":"CoRR"},{"key":"ref20","article-title":"Adaptive federated optimization","volume":"abs 2003 295","author":"reddi","year":"2020","journal-title":"CoRR"},{"key":"ref22","first-page":"9597","article-title":"Lookahead optimizer: k steps forward, 1 step back","author":"zhang","year":"2019","journal-title":"Advances in Neural IInformation Processing Systems"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9052983"},{"journal-title":"Cifar-10 (canadian institute for advanced research)","year":"0","author":"krizhevsky","key":"ref24"},{"key":"ref23","article-title":"Escaping saddle points faster with stochastic momentum","author":"wang","year":"0","journal-title":"International Conference on Learning Representations"}],"event":{"name":"2021 IEEE International Symposium on Information Theory (ISIT)","start":{"date-parts":[[2021,7,12]]},"location":"Melbourne, Australia","end":{"date-parts":[[2021,7,20]]}},"container-title":["2021 IEEE International Symposium on Information Theory (ISIT)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9517708\/9517709\/09517850.pdf?arnumber=9517850","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:44:57Z","timestamp":1652197497000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9517850\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,12]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/isit45174.2021.9517850","relation":{},"subject":[],"published":{"date-parts":[[2021,7,12]]}}}