{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T21:36:23Z","timestamp":1778621783761,"version":"3.51.4"},"reference-count":31,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"EPSRC DTP Studentship"},{"name":"Royal Society International Exchanges","award":["IEC\/NSFC\/211460"],"award-info":[{"award-number":["IEC\/NSFC\/211460"]}]},{"name":"EU Horizon 2020 INITIATE Project","award":["101008297"],"award-info":[{"award-number":["101008297"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Comput."],"published-print":{"date-parts":[[2023,6,1]]},"DOI":"10.1109\/tc.2022.3212631","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T20:13:59Z","timestamp":1665432839000},"page":"1804-1814","source":"Crossref","is-referenced-by-count":18,"title":["Accelerating Federated Learning With a Global Biased Optimiser"],"prefix":"10.1109","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6344-9364","authenticated-orcid":false,"given":"Jed","family":"Mills","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Exeter, Exeter, U.K."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5406-8420","authenticated-orcid":false,"given":"Jia","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Exeter, Exeter, U.K."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1395-7314","authenticated-orcid":false,"given":"Geyong","family":"Min","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Exeter, Exeter, U.K."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4136-4735","authenticated-orcid":false,"given":"Rui","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Exeter, Exeter, U.K."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siwei","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Exeter, Exeter, U.K."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2487-2148","authenticated-orcid":false,"given":"Jin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Exeter, Exeter, U.K."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1561\/9781680837896"},{"key":"ref2","first-page":"1273","article-title":"Communication-efficient learning of deep networks\n                        from decentralized data","volume-title":"Proc. Int. Conf.\n                        Artif. Intell. Statist.","author":"McMahan"},{"key":"ref3","article-title":"Adaptive federated\n                    optimization","volume-title":"Proc. Int. Conf. Learn.\n                        Representations","author":"Reddi"},{"key":"ref4","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for\n                        federated learning","volume-title":"Proc. Int. Conf.\n                        Mach. Learn.","author":"Karimireddy"},{"key":"ref5","first-page":"374","article-title":"Towards federated learning at scale: System\n                        design","volume-title":"Proc. Conf. Mach. Learn.\n                        Syst.","author":"Bonawitz"},{"key":"ref6","first-page":"429","article-title":"Federated optimization in heterogeneous\n                        networks","volume-title":"Proc. Mach. Learn.\n                        Syst.","author":"Li"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-67661-2_21"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.2975189"},{"key":"ref9","first-page":"28663","article-title":"Breaking the centralized barrier for\n                        cross-device federated learning","volume":"34","author":"Karimireddy","year":"2021"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2020.2994391"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2021.3072033"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2020.2992755"},{"key":"ref13","first-page":"2917","article-title":"Federated multi-armed bandits with\n                        personalization","volume-title":"Proc. Int. Conf. Artif.\n                        Intell. Statist.","author":"Shi"},{"key":"ref14","article-title":"Federated learning based on dynamic\n                        regularization","volume-title":"Proc. Int. Conf. Learn.\n                        Representations","author":"Acar"},{"key":"ref15","first-page":"2575","article-title":"Convergence and accuracy trade-offs in federated\n                        learning and meta-learning","volume-title":"Proc. Int.\n                        Conf. Artif. Intell. Statist.","author":"Charles"},{"key":"ref16","first-page":"6692","article-title":"From local SGD to local fixed-point methods for\n                        federated learning","volume-title":"Proc. Int. Conf.\n                        Mach. Learn.","author":"Malinovskiy"},{"key":"ref17","first-page":"10  334","article-title":"Is local SGD better than minibatch\n                        SGD?","volume-title":"Proc. Int. Conf. Mach.\n                        Learn.","author":"Woodworth"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2021.3099723"},{"key":"ref19","first-page":"315","article-title":"Accelerating stochastic gradient descent using\n                        predictive variance reduction","volume-title":"Proc. Adv.\n                        Neural Informat. Process. Syst.","author":"Johnson"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683546"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2956615"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.005.210108"},{"key":"ref23","article-title":"RMSprop, coursera: Neural netwroks for machine\n                        learning","author":"Tieleman","year":"2012"},{"key":"ref24","article-title":"Adam: A method for stochastic\n                        optimization","volume-title":"Proc. Int. Conf. Learn.\n                        Representations","author":"Kingma"},{"key":"ref25","article-title":"A simple convergence proof of adam and\n                        adagrad","author":"D\u00e9fossez","year":"2020"},{"key":"ref26","article-title":"Leaf: A benchmark for federated\n                        settings","volume-title":"Proc. NeurIPS Workshop\n                        Federated Learnign Data Privacy Confidentiality","author":"Caldas"},{"key":"ref27","first-page":"9367","article-title":"Descending through a crowded valley - benchmarking\n                        deep learning optimizers","volume-title":"Proc. Int.\n                        Conf. Mach. Learn.","author":"Schmidt"},{"key":"ref28","article-title":"On the convergence of adaptive gradient methods for\n                        nonconvex optimization","volume-title":"Proc. NeurIPS\n                        Workshop Optim. Mach. Learn.","author":"Zhou"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3023905"},{"key":"ref30","article-title":"Can federated learning save the\n                        planet?","volume-title":"Proc. NeurIPS Workshop Tackling\n                        Climate Change Mach. Learn.","author":"Qiu"},{"key":"ref31","article-title":"Measuring the algorithmic efficiency of neural\n                        networks","author":"Hernandez","year":"2020"}],"container-title":["IEEE Transactions on Computers"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/12\/10122189\/09913718.pdf?arnumber=9913718","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T04:35:36Z","timestamp":1725165336000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9913718\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,1]]},"references-count":31,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tc.2022.3212631","relation":{},"ISSN":["0018-9340","1557-9956","2326-3814"],"issn-type":[{"value":"0018-9340","type":"print"},{"value":"1557-9956","type":"electronic"},{"value":"2326-3814","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,1]]}}}