{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:01:09Z","timestamp":1783555269019,"version":"3.55.0"},"reference-count":20,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"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":[[2020,6]]},"DOI":"10.1109\/isit44484.2020.9174245","type":"proceedings-article","created":{"date-parts":[[2020,8,24]],"date-time":"2020-08-24T17:28:01Z","timestamp":1598290081000},"page":"2610-2615","source":"Crossref","is-referenced-by-count":38,"title":["The Communication-Aware Clustered Federated Learning Problem"],"prefix":"10.1109","author":[{"given":"Nir","family":"Shlezinger","sequence":"first","affiliation":[{"name":"Weizmann Institute of Science,Faculty of Math and CS,Rehovot,Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano","family":"Rini","sequence":"additional","affiliation":[{"name":"National Chiao Tung University,Department of EE,Hsinchu,Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonina C.","family":"Eldar","sequence":"additional","affiliation":[{"name":"Weizmann Institute of Science,Faculty of Math and CS,Rehovot,Israel"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054168"},{"key":"ref11","first-page":"4424","article-title":"Federated multi-task learning","author":"smith","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref12","article-title":"Improving federated learning personalization via model agnostic meta learning","author":"jiang","year":"2019"},{"key":"ref13","article-title":"Agnostic federated learning","author":"mohri","year":"2019"},{"key":"ref14","article-title":"Clustered federated learning: Model-agnostic distributed multi-task optimization under privacy constraints","author":"sattler","year":"2019"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref16","first-page":"367","article-title":"Multiple source adaptation and the Renyi divergence","author":"mansour","year":"2009","journal-title":"Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2014.2320500"},{"key":"ref18","article-title":"Federated learning: Collaborative machine learning without centralized training data","volume":"3","author":"mcmahan","year":"2017","journal-title":"Google Research Blog"},{"key":"ref19","article-title":"Mixture density networks","author":"bishop","year":"1994"},{"key":"ref4","article-title":"Federated learning: Challenges, methods, and future directions","author":"li","year":"2019"},{"key":"ref3","article-title":"Federated learning: Strategies for improving communication efficiency","author":"kone?n?","year":"2016"},{"key":"ref6","first-page":"1709","article-title":"QSGD: Communication-efficient SGD via gradient quantization and encoding","author":"alistarh","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2019.2921977"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/NCA.2017.8171350"},{"key":"ref7","article-title":"Deep gradient compression: Reducing the communication bandwidth for distributed training","author":"lin","year":"2017"},{"key":"ref2","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2016"},{"key":"ref1","article-title":"Towards federated learning at scale: System design","author":"bonawitz","year":"2019"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1045"},{"key":"ref20","author":"mclachlan","year":"2004","journal-title":"Finite Mixture Models"}],"event":{"name":"2020 IEEE International Symposium on Information Theory (ISIT)","location":"Los Angeles, CA, USA","start":{"date-parts":[[2020,6,21]]},"end":{"date-parts":[[2020,6,26]]}},"container-title":["2020 IEEE International Symposium on Information Theory (ISIT)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9166581\/9173928\/09174245.pdf?arnumber=9174245","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T20:34:21Z","timestamp":1773347661000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9174245\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/isit44484.2020.9174245","relation":{},"subject":[],"published":{"date-parts":[[2020,6]]}}}