{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T18:41:21Z","timestamp":1762368081024,"version":"build-2065373602"},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T00:00:00Z","timestamp":1756598400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T00:00:00Z","timestamp":1756598400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,31]]},"DOI":"10.1109\/mlsp62443.2025.11204301","type":"proceedings-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T17:15:52Z","timestamp":1761326152000},"page":"1-6","source":"Crossref","is-referenced-by-count":0,"title":["Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning"],"prefix":"10.1109","author":[{"given":"Karlo","family":"Palenzuela","sequence":"first","affiliation":[{"name":"Ume&#x00E5; University,Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Dadras","sequence":"additional","affiliation":[{"name":"Ume&#x00E5; University,Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alp","family":"Yurtsever","sequence":"additional","affiliation":[{"name":"Ume&#x00E5; University,Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tommy","family":"L\u00f6fstedt","sequence":"additional","affiliation":[{"name":"Ume&#x00E5; University,Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"journal-title":"Federated learning: Strategies for improving communication efficiency","year":"2016","author":"Kone\u010dn\u1ef3","key":"ref1"},{"key":"ref2","article-title":"Tighter theory for local SGD on identical and heterogeneous data","volume":"108","author":"Khaled","year":"2020","journal-title":"Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics"},{"key":"ref3","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"},{"key":"ref4","article-title":"ProxSkip: Yes! Local gradient steps provably lead to communication acceleration! Finally!","volume":"162","author":"Mishchenko","year":"2022","journal-title":"Proceedings of the 39th International Conference on Machine Learning"},{"key":"ref5","article-title":"Projection efficient subgradient method and optimal nonsmooth Frank-Wolfe method","volume":"33","author":"Thekumparampil","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"journal-title":"GradSkip: Communication-accelerated local gradient methods with better computational complexity","year":"2022","author":"Maranjyan","key":"ref6"},{"key":"ref7","article-title":"Tighter analysis for ProxSkip","volume":"202","author":"Hu","year":"2023","journal-title":"Proceedings of the 40th International Conference on Machine Learning"},{"journal-title":"Communication efficiency in federated learning: Achievements and challenges","year":"2021","author":"Shahid","key":"ref8"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfranklin.2022.12.053"},{"key":"ref10","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"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"journal-title":"Federated learning with non-iid data","year":"2018","author":"Zhao","key":"ref12"},{"key":"ref13","article-title":"Local SGD converges fast and communicates little","author":"Stich","year":"2019","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref15","article-title":"Local SGD: Unified theory and new efficient methods","volume":"130","author":"Gorbunov","year":"2021","journal-title":"Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS)"},{"key":"ref16","article-title":"First analysis of local GD on heterogeneous data","author":"Khaled","year":"2019","journal-title":"NeurIPS 2019 Workshop on Federated Learning for Data Privacy and Confidentiality"},{"key":"ref17","first-page":"6281","article-title":"Minibatch vs local sgd for heterogeneous distributed learning","volume":"33","author":"Woodworth","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"journal-title":"On the convergence of local descent methods in federated learning","year":"2019","author":"Haddadpour","key":"ref18"},{"key":"ref19","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li","year":"2020","journal-title":"Proceedings of machine learning and systems"},{"journal-title":"TAMUNA: Accelerated federated learning with local training and partial participation","year":"2023","author":"Condat","key":"ref20"},{"journal-title":"LoCoDL: Communication-efficient distributed learning with local training and compression","year":"2024","author":"Condat","key":"ref21"},{"journal-title":"FedComLoc: Communication-efficient distributed training of sparse and quantized models","year":"2024","author":"Yi","key":"ref22"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.87.23.9193"},{"volume-title":"CVX: Matlab software for disciplined convex programming, version 2.1","year":"2014","key":"ref24"}],"event":{"name":"2025 IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP)","start":{"date-parts":[[2025,8,31]]},"location":"Istanbul, Turkiye","end":{"date-parts":[[2025,9,3]]}},"container-title":["2025 IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP)"],"original-title":[],"link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/xplorestaging.ieee.org\/ielx8\/11204201\/11204202\/11204301.pdf?arnumber=11204301","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T18:37:09Z","timestamp":1762367829000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/ieeexplore.ieee.org\/document\/11204301\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,31]]},"references-count":24,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1109\/mlsp62443.2025.11204301","relation":{},"subject":[],"published":{"date-parts":[[2025,8,31]]}}}