{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T06:59:29Z","timestamp":1769929169507,"version":"3.49.0"},"reference-count":41,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T00:00:00Z","timestamp":1655078400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T00:00:00Z","timestamp":1655078400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,13]]},"DOI":"10.23919\/ifipnetworking55013.2022.9829798","type":"proceedings-article","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T16:42:14Z","timestamp":1658508134000},"page":"1-9","source":"Crossref","is-referenced-by-count":4,"title":["Joint Consensus Matrix Design and Resource Allocation for Decentralized Learning"],"prefix":"10.23919","author":[{"given":"Jingrong","family":"Wang","sequence":"first","affiliation":[{"name":"University of Toronto,Department of Electrical and Computer Engineering,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ben","family":"Liang","sequence":"additional","affiliation":[{"name":"University of Toronto,Department of Electrical and Computer Engineering,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongwen","family":"Zhu","sequence":"additional","affiliation":[{"name":"Ericsson Global AI Accelerator Montr&#x00E9;al,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emmanuel Thepie","family":"Fapi","sequence":"additional","affiliation":[{"name":"Ericsson Global AI Accelerator Montr&#x00E9;al,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hardik","family":"Dalal","sequence":"additional","affiliation":[{"name":"Ericsson Global AI Accelerator Montr&#x00E9;al,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2016.2611964"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-34097-0_7"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737367"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2012.6426375"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2005.1498447"},{"key":"ref30","article-title":"Gradient sparsification for communication-efficient distributed optimization","author":"wangni","year":"0","journal-title":"Proc NeurIPS"},{"key":"ref37","first-page":"2348","article-title":"Decentralized gradient methods: does topology matter?","volume":"108","author":"neglia","year":"0","journal-title":"Proc AISTATS"},{"key":"ref36","first-page":"13601","article-title":"Communication trade-offs for Local-SGD with large step size","volume":"32","author":"dieuleveut","year":"0","journal-title":"Proc NeurIPS"},{"key":"ref35","article-title":"Local SGD converges fast and communicates little","author":"stich","year":"0","journal-title":"Proc ICLR"},{"key":"ref34","first-page":"2933","article-title":"Distributed online optimization over a heterogeneous network with any-batch mirror descent","author":"eshraghi","year":"0","journal-title":"Proc ICML"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2006.377041"},{"key":"ref40","year":"0","journal-title":"MNIST Handwritten Digit Database"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS.2007.378145"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9196822"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-47436-2_67"},{"key":"ref14","first-page":"953","article-title":"Network topology and communication-computation tradeoffs in decentralized optimization","volume":"106","author":"nedi?","year":"0","journal-title":"Proceedings of the IEEE"},{"key":"ref15","first-page":"5336","article-title":"Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent","author":"lian","year":"0","journal-title":"Proc NeurIPS"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054065"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2570808"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737602"},{"key":"ref19","article-title":"Efficient and reliable overlay networks for decentralized federated learning","author":"hua","year":"2021","journal-title":"ArXiv Preprint"},{"key":"ref28","article-title":"Deep gradient compression: Reducing the communication bandwidth for distributed training","author":"lin","year":"0","journal-title":"Proc ICLR 2017"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysconle.2004.02.022"},{"key":"ref27","article-title":"Throughput-optimal topology design for cross-silo federated learning","author":"marfoq","year":"0","journal-title":"Proc NeurIPS"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3377454"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2006.08.010"},{"key":"ref29","first-page":"1709","article-title":"QSGD: Communication-efficient SGD via gradient quantization and encoding","volume":"30","author":"alistarh","year":"0","journal-title":"Proc NeurIPS"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1137\/S0036144503423264"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2005.861710"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1137\/070689413"},{"key":"ref2","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"0","journal-title":"Proc AISTATS"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.laa.2006.03.006"},{"key":"ref1","first-page":"19","article-title":"Communication efficient distributed machine learning with the parameter server","volume":"27","author":"li","year":"0","journal-title":"Proc NeurIPS"},{"key":"ref20","first-page":"1280","article-title":"Optimal topology design for dynamic net-works","author":"dai","year":"0","journal-title":"Proc IEEE CDC-ECC"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2014.7039730"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysconle.2009.08.005"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2018.2813426"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TCNS.2015.2503561"},{"key":"ref26","article-title":"Optimal network topology design in multiagent systems for efficient average consensus","author":"rafiee","year":"0","journal-title":"Proc IEEE CDC"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488817"}],"event":{"name":"2022 IFIP Networking Conference (IFIP Networking)","location":"Catania, Italy","start":{"date-parts":[[2022,6,13]]},"end":{"date-parts":[[2022,6,16]]}},"container-title":["2022 IFIP Networking Conference (IFIP Networking)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9829715\/9829755\/09829798.pdf?arnumber=9829798","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T21:50:17Z","timestamp":1661809817000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9829798\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,13]]},"references-count":41,"URL":"https:\/\/doi.org\/10.23919\/ifipnetworking55013.2022.9829798","relation":{},"subject":[],"published":{"date-parts":[[2022,6,13]]}}}