{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T05:37:48Z","timestamp":1784785068243,"version":"3.55.0"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"RGC RIF","award":["R6021-20"],"award-info":[{"award-number":["R6021-20"]}]},{"name":"RGC GRF","award":["16209120"],"award-info":[{"award-number":["16209120"]}]},{"name":"RGC GRF","award":["16200221"],"award-info":[{"award-number":["16200221"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Parallel Distrib. Syst."],"published-print":{"date-parts":[[2023,3,1]]},"DOI":"10.1109\/tpds.2022.3230938","type":"journal-article","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T18:48:42Z","timestamp":1671648522000},"page":"909-922","source":"Crossref","is-referenced-by-count":161,"title":["GossipFL: A Decentralized Federated Learning Framework With Sparsified and Adaptive Communication"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8769-9974","authenticated-orcid":false,"given":"Zhenheng","family":"Tang","sequence":"first","affiliation":[{"name":"Hong Kong Baptist University, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1418-5160","authenticated-orcid":false,"given":"Shaohuai","family":"Shi","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2083-9105","authenticated-orcid":false,"given":"Bo","family":"Li","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9745-4372","authenticated-orcid":false,"given":"Xiaowen","family":"Chu","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","article-title":"Federated learning: Strategies for improving communication efficiency","volume-title":"Proc. Conf. Neural Inf. Process. Syst. Workshop","author":"Kone\u010dn\u1ef3"},{"key":"ref2","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.112.2100706"},{"key":"ref5","first-page":"1","article-title":"On the convergence of FedAvg on non-IID data","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref6","first-page":"21111","article-title":"Virtual homogeneity learning: Defending against data heterogeneity in federated learning","volume-title":"Proc. 39th Int. Conf. Mach. Learn.","author":"Tang"},{"key":"ref7","first-page":"629","article-title":"Gaia: Geo-distributed machine learning approaching LAN speeds","volume-title":"Proc. 14th USENIX Conf. Netw. Syst. Des. Implementation","author":"Hsieh"},{"key":"ref8","article-title":"Towards federated learning at scale: System design","author":"Bonawitz","year":"2019"},{"key":"ref9","first-page":"7202","article-title":"Distributed learning over unreliable networks","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Yu"},{"key":"ref10","article-title":"FetchSGD: Communication-efficient federated learning with sketching","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Rothchild"},{"key":"ref11","article-title":"How to train your neural network: A comparative evaluation","author":"Lin","year":"2021"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3095077"},{"key":"ref13","first-page":"5336","article-title":"Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Lian"},{"key":"ref14","first-page":"7663","article-title":"Communication compression for decentralized training","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Tang"},{"key":"ref15","article-title":"The non-IID data quagmire of decentralized machine learning","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Hsieh"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-22496-7_5"},{"key":"ref17","first-page":"1","article-title":"Decentralized federated learning: A segmented gossip approach","volume-title":"Proc. Int. Workshop Federated Mach. Learn. User Privacy Data Confidentiality","author":"Hu"},{"key":"ref18","first-page":"1","article-title":"Decentralized deep learning with arbitrary communication compression","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Koloskova"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3046440"},{"key":"ref20","article-title":"Communication-efficient distributed deep learning: A comprehensive survey","author":"Tang","year":"2020"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS47774.2020.00153"},{"key":"ref22","first-page":"493","article-title":"BatchCrypt: Efficient homomorphic encryption for cross-silo federated learning","volume-title":"Proc. USENIX Conf. Usenix Annu. Tech. Conf.","author":"Zhang"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.011.2000263"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3429252"},{"key":"ref25","article-title":"Tackling system and statistical heterogeneity for federated learning with adaptive client sampling","author":"Luo","year":"2021"},{"key":"ref26","first-page":"3478","article-title":"Decentralized stochastic optimization and gossip algorithms with compressed communication","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Koloskova"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICC47138.2019.9123209"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2005.1498447"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2006.874516"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/SFCS.2000.892324"},{"key":"ref31","volume-title":"Algorithmic Graph Theory","author":"Gibbons","year":"1985"},{"key":"ref32","article-title":"FedCV: A federated learning framework for diverse computer vision tasks","author":"He","year":"2021"},{"key":"ref33","article-title":"Real-world image datasets for federated learning","author":"Luo","year":"2019"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_2"},{"key":"ref35","first-page":"9706","article-title":"Variational model inversion attacks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11728"},{"key":"ref37","first-page":"3252","article-title":"Error feedback fixes SignSGD and other gradient compression schemes","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Karimireddy"},{"key":"ref38","article-title":"MNIST handwritten digit database","volume":"2","author":"LeCun","year":"2010","journal-title":"AT&T Labs"},{"key":"ref39","article-title":"CIFAR-10 (Canadian Institute for Advanced Research)","author":"Krizhevsky","year":"2010"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref41","article-title":"FedML: A research library and benchmark for federated machine learning","author":"He","year":"2020"},{"issue":"1","key":"ref42","first-page":"219","article-title":"The Gn,mphase transition is not hard for the hamiltonian cycle problem","volume":"9","author":"Vandegriend","year":"1998","journal-title":"J. Artif. Int. Res."},{"key":"ref43","first-page":"1509","article-title":"TernGrad: Ternary gradients to reduce communication in distributed deep learning","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Wen"},{"key":"ref44","first-page":"1707","article-title":"QSGD: Communication-efficient SGD via gradient quantization and encoding","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Alistarh"},{"key":"ref45","first-page":"1","article-title":"Deep gradient compression: Reducing the communication bandwidth for distributed training","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lin"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/473"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155269"},{"key":"ref48","first-page":"401","article-title":"Towards scalable distributed training of deep learning on public cloud clusters","volume-title":"Proc. 4th MLSys Conf.","author":"Shi"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3485929"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1137\/16M1081257"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.11.002"},{"key":"ref52","article-title":"GossipGraD: Scalable deep learning using gossip communication based asynchronous gradient descent","author":"Daily","year":"2018"},{"key":"ref53","first-page":"3043","article-title":"Asynchronous decentralized parallel stochastic gradient descent","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Lian"}],"container-title":["IEEE Transactions on Parallel and Distributed Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/71\/10012125\/09996127.pdf?arnumber=9996127","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T09:34:00Z","timestamp":1725960840000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9996127\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,1]]},"references-count":53,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tpds.2022.3230938","relation":{},"ISSN":["1045-9219","1558-2183","2161-9883"],"issn-type":[{"value":"1045-9219","type":"print"},{"value":"1558-2183","type":"electronic"},{"value":"2161-9883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,1]]}}}