{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T08:10:21Z","timestamp":1759133421672},"reference-count":10,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"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":[[2020,3]]},"DOI":"10.1109\/ciss48834.2020.1570616094","type":"proceedings-article","created":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T02:27:40Z","timestamp":1588904860000},"page":"1-6","source":"Crossref","is-referenced-by-count":10,"title":["An Optimal Stopping Approach for Iterative Training in Federated Learning"],"prefix":"10.1109","author":[{"given":"Pengfei","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Lei","family":"Ying","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2408776.2408794"},{"key":"ref3","article-title":"Federated learning for mobile keyboard prediction","author":"hard","year":"2018","journal-title":"arXiv preprint arXiv 1811 03604"},{"key":"ref10","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2016","journal-title":"arXiv preprint arXiv 1602 05629"},{"key":"ref6","article-title":"Adaptive communication strategies to achieve the best error-runtime trade-off in local-update sgd","author":"wang","year":"2018","journal-title":"arXiv preprint arXiv 1810 06008"},{"key":"ref5","first-page":"1223","article-title":"Large scale distributed deep networks","author":"dean","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"ref8","article-title":"An optimal stopping approach for iterative training in federated learning","author":"jiang","year":"2019","journal-title":"Arizona State Technical Report"},{"key":"ref7","article-title":"Cooperative sgd: A unified framework for the design and analysis of communication-efficient sgd algorithms","author":"wang","year":"2018","journal-title":"arXiv preprint arXiv 1808 07536"},{"key":"ref2","article-title":"Federated learning of out-of-vocabulary words","author":"chen","year":"2019","journal-title":"arXiv preprint arXiv 1903 11593"},{"article-title":"Optimal stopping and applications","year":"2012","author":"ferguson","key":"ref9"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"}],"event":{"name":"2020 54th Annual Conference on Information Sciences and Systems (CISS)","start":{"date-parts":[[2020,3,18]]},"location":"Princeton, NJ, USA","end":{"date-parts":[[2020,3,20]]}},"container-title":["2020 54th Annual Conference on Information Sciences and Systems (CISS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9081570\/9086165\/09086230.pdf?arnumber=9086230","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T15:47:05Z","timestamp":1656344825000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9086230\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3]]},"references-count":10,"URL":"https:\/\/doi.org\/10.1109\/ciss48834.2020.1570616094","relation":{},"subject":[],"published":{"date-parts":[[2020,3]]}}}