{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T03:31:13Z","timestamp":1784086273223,"version":"3.55.0"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62125109"],"award-info":[{"award-number":["62125109"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["T2122024"],"award-info":[{"award-number":["T2122024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62431017"],"award-info":[{"award-number":["62431017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62401357"],"award-info":[{"award-number":["62401357"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62320106003"],"award-info":[{"award-number":["62320106003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62371288"],"award-info":[{"award-number":["62371288"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61931023"],"award-info":[{"award-number":["61931023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61932022"],"award-info":[{"award-number":["61932022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62120106007"],"award-info":[{"award-number":["62120106007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1109\/tpami.2024.3469188","type":"journal-article","created":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T17:45:57Z","timestamp":1727372757000},"page":"67-83","source":"Crossref","is-referenced-by-count":16,"title":["Stabilizing and Accelerating Federated Learning on Heterogeneous Data With Partial Client Participation"],"prefix":"10.1109","volume":"47","author":[{"given":"Hao","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2888-594X","authenticated-orcid":false,"given":"Chenglin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2522-5778","authenticated-orcid":false,"given":"Wenrui","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9923-8016","authenticated-orcid":false,"given":"Ziyang","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9694-9880","authenticated-orcid":false,"given":"Junni","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4552-0029","authenticated-orcid":false,"given":"Hongkai","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"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 from decentralized data","volume-title":"Proc. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref3","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy"},{"key":"ref4","first-page":"14606","article-title":"Linear convergence in federated learning: Tackling client heterogeneity and sparse gradients","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Mitra"},{"key":"ref5","first-page":"499","article-title":"Stability and generalization","volume":"2","author":"Bousquet","year":"2002","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"ref6","first-page":"55","article-title":"Stability of randomized learning algorithms","volume":"6","author":"Elisseeff","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref7","first-page":"1225","article-title":"Train faster, generalize better: Stability of stochastic gradient descent","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hardt"},{"key":"ref8","article-title":"Stability and convergence trade-off of iterative optimization algorithms","author":"Chen","year":"2018"},{"key":"ref9","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","author":"Li"},{"key":"ref10","article-title":"Federated learning based on dynamic regularization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Acar"},{"key":"ref11","first-page":"28663","article-title":"Breaking the centralized barrier for cross-device federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Karimireddy"},{"key":"ref12","first-page":"7184","article-title":"On the linear speedup analysis of communication efficient momentum SGD for distributed non-convex optimization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yu"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-91578-4"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2906207"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2484339"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2840719"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3033286"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3129809"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3195956"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3196503"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3243080"},{"key":"ref22","article-title":"A field guide to federated optimization","author":"Wang","year":"2021"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref24","first-page":"7057","article-title":"FedSplit: An algorithmic framework for fast federated optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Pathak"},{"key":"ref25","first-page":"6692","article-title":"From local SGD to local fixed-point methods for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Malinovskiy"},{"key":"ref26","article-title":"Adaptive federated optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Reddi"},{"key":"ref27","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Hsu","year":"2019"},{"key":"ref28","article-title":"Cooperative SGD: A unified framework for the design and analysis of communication-efficient SGD algorithms","author":"Wang","year":"2018"},{"key":"ref29","article-title":"Local SGD converges fast and communicates little","author":"Stich","year":"2018"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015693"},{"issue":"1","key":"ref31","first-page":"9613","article-title":"The error-feedback framework: Better rates for SGD with delayed gradients and compressed updates","volume":"21","author":"Stich","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref32","article-title":"On the convergence of FedAvg on Non-IID data","author":"Li","year":"2019"},{"key":"ref33","first-page":"4519","article-title":"Tighter theory for local SGD on identical and heterogeneous data","volume-title":"Porc. Int. Conf. Artif. Intell. Statist.","author":"Khaled","year":"2020"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2019.2937496"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3090331"},{"key":"ref36","first-page":"5332","article-title":"Federated accelerated stochastic gradient descent","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yuan"},{"key":"ref37","article-title":"Achieving linear speedup with partial worker participation in Non-IID federated learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Yang"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1137\/16M1080173"},{"key":"ref39","article-title":"Averaging weights leads to wider optima and better generalization","author":"Izmailov","year":"2018"},{"key":"ref40","article-title":"Stochastic weight averaging in parallel: Large-batch training that generalizes well","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Gupta"},{"key":"ref41","article-title":"What do we mean by generalization in federated learning?","author":"Yuan","year":"2021"},{"key":"ref42","first-page":"5949","article-title":"Exploring generalization in deep learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Neyshabur"},{"key":"ref43","first-page":"5686","article-title":"Consensus control for decentralized deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kong"},{"key":"ref44","first-page":"5381","article-title":"A unified theory of decentralized SGD with changing topology and local updates","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Koloskova"},{"key":"ref45","article-title":"Federated learning with Non-IID data","author":"Zhao","year":"2018"},{"key":"ref46","article-title":"Federated learning via synthetic data","author":"Goetz","year":"2020"},{"key":"ref47","article-title":"Communication-efficient on-device machine learning: Federated distillation and augmentation under Non-IID private data","author":"Jeong","year":"2018"},{"key":"ref48","first-page":"950","article-title":"A simple weight decay can improve generalization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Krogh"},{"key":"ref49","article-title":"Quasi-global momentum: Accelerating decentralized deep learning on heterogeneous data","author":"Lin","year":"2021"},{"key":"ref50","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref52","article-title":"SlowMo: Improving communication-efficient distributed SGD with slow momentum","author":"Wang","year":"2019"},{"key":"ref53","article-title":"Differentially private federated learning: A client level perspective","author":"Geyer","year":"2017"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.15180"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2022.3187678"},{"key":"ref56","first-page":"74753","article-title":"FedNAR: Federated optimization with normalized annealing regularization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Li"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01177"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3300886"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/IROS55552.2023.10342134"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108218"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/10777928\/10696955.pdf?arnumber=10696955","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T06:30:13Z","timestamp":1733380213000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10696955\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":60,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2024.3469188","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]}}}