{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,7]],"date-time":"2024-09-07T18:58:12Z","timestamp":1725735492415},"reference-count":16,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,9,19]],"date-time":"2021-09-19T00:00:00Z","timestamp":1632009600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,19]],"date-time":"2021-09-19T00:00:00Z","timestamp":1632009600000},"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":[[2021,9,19]]},"DOI":"10.1109\/icip42928.2021.9506725","type":"proceedings-article","created":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T21:08:41Z","timestamp":1629752921000},"page":"1234-1238","source":"Crossref","is-referenced-by-count":3,"title":["Federated Trace: A Node Selection Method for More Efficient Federated Learning"],"prefix":"10.1109","author":[{"given":"Zirui","family":"Zhu","sequence":"first","affiliation":[{"name":"Tsinghua University,Department of Computer Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lifeng","family":"Sun","sequence":"additional","affiliation":[{"name":"Tsinghua University,Department of Computer Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"ref11","article-title":"Active federated learning","author":"goetz","year":"2019","journal-title":"arXiv preprint arXiv 1909 11324"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/584091.584093"},{"key":"ref13","article-title":"k-means++: The advantages of careful seeding","author":"arthur","year":"2006","journal-title":"Tech Rep"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref15","article-title":"Fashionmnist: a novel image dataset for benchmarking machine learning algorithms","author":"xiao","year":"2017","journal-title":"arXiv preprint arXiv 1708 07747"},{"key":"ref16","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"harry hsu","year":"2019","journal-title":"arXiv preprint arXiv 1909 01771"},{"key":"ref4","article-title":"Federated learning of deep networks using model averaging. corr abs\/1602.05629 (2016)","author":"brendan mcmahan","year":"2016","journal-title":"arXiv preprint arXiv 1602 05629"},{"key":"ref3","article-title":"Federated learning: Strategies for improving communication efficiency","author":"kone?n\u00fd","year":"2016","journal-title":"arXiv preprint arXiv 1610 05492"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/VCIP.2018.8698609"},{"key":"ref5","article-title":"Federated learning with non-iid data","author":"zhao","year":"2018","journal-title":"arXiv preprint arXiv 1806 00582"},{"key":"ref8","article-title":"Federated optimization in heterogeneous networks","author":"li","year":"2018","journal-title":"arXiv preprint arXiv 1812 08942"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP40778.2020.9190968"},{"key":"ref2","article-title":"Federated optimization: Distributed optimization beyond the datacenter","author":"kone?n\u00fd","year":"2015","journal-title":"arXiv preprint arXiv 1511 00353"},{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"brendan mcmahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref9","article-title":"Overcoming forgetting in federated learning on non-iid data","author":"shoham","year":"2019","journal-title":"arXiv preprint arXiv 1910 07796"}],"event":{"name":"2021 IEEE International Conference on Image Processing (ICIP)","start":{"date-parts":[[2021,9,19]]},"location":"Anchorage, AK, USA","end":{"date-parts":[[2021,9,22]]}},"container-title":["2021 IEEE International Conference on Image Processing (ICIP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9506008\/9506009\/09506725.pdf?arnumber=9506725","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T21:13:05Z","timestamp":1655154785000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9506725\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,19]]},"references-count":16,"URL":"https:\/\/doi.org\/10.1109\/icip42928.2021.9506725","relation":{},"subject":[],"published":{"date-parts":[[2021,9,19]]}}}