{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T20:27:18Z","timestamp":1783542438148,"version":"3.55.0"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"8","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"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":["62225113"],"award-info":[{"award-number":["62225113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovative Research Group Project of Hubei Province","award":["2024AFA017"],"award-info":[{"award-number":["2024AFA017"]}]},{"name":"New Cornerstone Science Foundation through the XPLORER PRIZE"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62576364"],"award-info":[{"award-number":["62576364"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2026B1515020071"],"award-info":[{"award-number":["2026B1515020071"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Basic Research Project (Natural Science Foundation) Basic Research Key Project","award":["JCYJ20241202124430041"],"award-info":[{"award-number":["JCYJ20241202124430041"]}]},{"name":"CCF-DiDi GAIA Collaborative Research Funds","award":["CCF-DiDi GAIA 202508"],"award-info":[{"award-number":["CCF-DiDi GAIA 202508"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1109\/tpami.2026.3677027","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T19:48:04Z","timestamp":1774381684000},"page":"9197-9209","source":"Crossref","is-referenced-by-count":0,"title":["Stability and Generalization for Distributed SGDA"],"prefix":"10.1109","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9534-1999","authenticated-orcid":false,"given":"Miaoxi","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Computer Science, National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2271-252X","authenticated-orcid":false,"given":"Yan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Sydney, Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5659-3464","authenticated-orcid":false,"given":"Li","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0059-8458","authenticated-orcid":false,"given":"Bo","family":"Du","sequence":"additional","affiliation":[{"name":"School of Computer Science, National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7225-5449","authenticated-orcid":false,"given":"Dacheng","family":"Tao","sequence":"additional","affiliation":[{"name":"College of Computing &#x0026; Data Science, Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"6175","article-title":"Stability and generalization of stochastic gradient methods for minimax problems","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lei"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.52202\/075280-1377"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref4","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arjovsky"},{"key":"ref5","first-page":"1310","article-title":"Certified adversarial robustness via randomized smoothing","volume-title":"Proc. Int. Conf. Mach. Learn","author":"Cohen"},{"key":"ref6","first-page":"1","article-title":"Generative adversarial imitation learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ho"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3287908"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.06083"},{"key":"ref9","first-page":"2164","article-title":"Learning controllable fair representations","volume-title":"Proc. 22nd Int. Conf. Artif. Intell. Statist.","author":"Song"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/90.993302"},{"key":"ref11","first-page":"8568","article-title":"Adversarial multiple source domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhao"},{"key":"ref12","first-page":"19683","article-title":"Federated minimax optimization: Improved convergence analyses and algorithms","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sharma"},{"key":"ref13","first-page":"11056","article-title":"A decentralized parallel algorithm for training generative adversarial nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref14","article-title":"Proximal gradient descent-ascent: Variable convergence under k $\\lbrace \\backslash L\\rbrace${\u2216L} geometry","author":"Chen","year":"2021"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2762"},{"key":"ref16","first-page":"25865","article-title":"A faster decentralized algorithm for nonconvex minimax problems","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Xian"},{"key":"ref17","article-title":"Stochastic smoothed gradient descent ascent for federated minimax optimization","author":"Shen","year":"2023"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1019"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0518"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-022-06239-z"},{"key":"ref21","first-page":"15111","article-title":"Distributionally robust federated averaging","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Deng"},{"key":"ref22","first-page":"21554","article-title":"Robust federated learning: The case of affine distribution shifts","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Reisizadeh"},{"key":"ref23","first-page":"1387","article-title":"Local stochastic gradient descent ascent: Convergence analysis and communication efficiency","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Deng"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0439"},{"key":"ref25","article-title":"Efficient algorithms for federated saddle point optimization","author":"Hou","year":"2021"},{"key":"ref26","article-title":"FedGAN: Federated generative adversarial networks for distributed data","author":"Rasouli","year":"2020"},{"key":"ref27","article-title":"Federated minimax optimization with client heterogeneity","author":"Sharma","year":"2023","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref28","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","volume":"2","author":"Li"},{"key":"ref29","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Hsu","year":"2019"},{"key":"ref30","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy"},{"key":"ref31","first-page":"21146","article-title":"Fednest: Federated bilevel, minimax, and compositional optimization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tarzanagh"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26246"},{"key":"ref33","first-page":"9648","article-title":"Federated compositional deep AUC maximization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0497"},{"key":"ref35","first-page":"1225","article-title":"Train faster, generalize better: Stability of stochastic gradient descent","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hardt"},{"key":"ref36","first-page":"499","article-title":"Stability and generalization","volume":"2","author":"Bousquet","year":"2002","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"ref37","first-page":"55","article-title":"Stability of randomized learning algorithms","volume":"6","author":"Elisseeff","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1142\/S0219530505000650"},{"key":"ref39","first-page":"2635","article-title":"Learnability, stability and uniform convergence","volume":"11","author":"Shalev-Shwartz","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref40","first-page":"3174","article-title":"Train simultaneously, generalize better: Stability of gradient-based minimax learners","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Farnia"},{"key":"ref41","first-page":"26523","article-title":"On the algorithmic stability of adversarial training","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Xing"},{"key":"ref42","first-page":"568","article-title":"Generalization bounds for stochastic saddle point problems","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Zhang"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2767"},{"key":"ref44","first-page":"2192","article-title":"Differentially private SGDA for minimax problems","volume-title":"Proc. Uncertainty Artif. Intell.","author":"Yang"},{"key":"ref45","first-page":"4615","article-title":"Agnostic federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Mohri"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2018.2817461"},{"key":"ref47","first-page":"13975","article-title":"Exponential graph is provably efficient for decentralized deep training","volume":"34","author":"Ying","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-91059-4_18"},{"issue":"4","key":"ref49","first-page":"643","article-title":"Gradient methods for minimizing functionals","volume":"3","author":"Polyak","year":"1963","journal-title":"Zhurnal Vychislitel\u2019noi Matematiki i Matematicheskoi Fiziki"},{"issue":"87\/89","key":"ref50","article-title":"A topological property of real analytic subsets","volume":"117","author":"Lojasiewicz","year":"1963","journal-title":"Coll. du CNRS Les \u00c9quations aux D\u00e9riv\u00e9es Partielles"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46128-1_50"},{"key":"ref52","article-title":"Stability and generalization for minibatch SGD and local SGD","author":"Lei","year":"2023"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139020411"},{"issue":"213","key":"ref54","first-page":"1","article-title":"Cooperative SGD: A unified framework for the design and analysis of local-update SGD algorithms","volume":"22","author":"Wang","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref55","first-page":"6083","article-title":"On gradient descent ascent for nonconvex-concave minimax problems","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lin"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/11595778\/11455079.pdf?arnumber=11455079","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T19:43:05Z","timestamp":1783539785000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11455079\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":55,"journal-issue":{"issue":"8"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2026.3677027","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":[[2026,8]]}}}