{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T09:04:11Z","timestamp":1769159051401,"version":"3.49.0"},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T00:00:00Z","timestamp":1652659200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T00:00:00Z","timestamp":1652659200000},"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":[[2022,5,16]]},"DOI":"10.1109\/icc45855.2022.9839079","type":"proceedings-article","created":{"date-parts":[[2022,8,11]],"date-time":"2022-08-11T19:37:11Z","timestamp":1660246631000},"page":"2828-2833","source":"Crossref","is-referenced-by-count":5,"title":["Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression"],"prefix":"10.1109","author":[{"given":"Francois","family":"Gauthier","sequence":"first","affiliation":[{"name":"Norwegian University of Science and Technology,Dept. of Electronic Systems,Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vinay Chakravarthi","family":"Gogineni","sequence":"additional","affiliation":[{"name":"Norwegian University of Science and Technology,Dept. of Electronic Systems,Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan","family":"Werner","sequence":"additional","affiliation":[{"name":"Norwegian University of Science and Technology,Dept. of Electronic Systems,Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yih-Fang","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Notre Dame,Dept. of Electrical Engineering,Notre Dame,IN,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anthony","family":"Kuh","sequence":"additional","affiliation":[{"name":"University of Hawaii,Dept. of Electrical and Computer Engineering,Hawaii,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/BigData52589.2021.9671924"},{"key":"ref11","article-title":"Asynchronous federated optimization","author":"xie","year":"2019"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378161"},{"key":"ref13","article-title":"Fedat: a communication-efficient federated learning method with asynchronous tiers under non-iid data","author":"chai","year":"2020"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2953131"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICC42927.2021.9500860"},{"key":"ref16","article-title":"Communication-efficient online federated learning framework for nonlinear regression","author":"gogineni","year":"2021"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref18","article-title":"Federated learning: Strategies for improving communication efficiency","author":"kone?ny","year":"2016"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3369583.3392686"},{"key":"ref4","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Artificial intel statis"},{"key":"ref3","article-title":"Federated learning with non-iid data","author":"zhao","year":"2018"},{"key":"ref6","article-title":"Federated learning: strategies for improving communication efficiency","author":"kone?ny","year":"2016"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT45174.2021.9517850"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICC42927.2021.9500632"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3037554"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref1","article-title":"Federated optimization: distributed machine learning for on-device intelligence","author":"kone?ny","year":"2016"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CSCloud-EdgeCom52276.2021.00038"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC49053.2021.9417459"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/SSP.2016.7551811"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2014.2327005"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TCSII.2021.3050725"},{"key":"ref23","first-page":"1","article-title":"Random features for large-scale kernel machines","volume":"3","author":"rahimi","year":"2007","journal-title":"Proc Conf on Neural Inf Proc Syst"}],"event":{"name":"ICC 2022 - IEEE International Conference on Communications","location":"Seoul, Korea, Republic of","start":{"date-parts":[[2022,5,16]]},"end":{"date-parts":[[2022,5,20]]}},"container-title":["ICC 2022 - IEEE International Conference on Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9837954\/9838246\/09839079.pdf?arnumber=9839079","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T20:02:43Z","timestamp":1663012963000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9839079\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,16]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/icc45855.2022.9839079","relation":{},"subject":[],"published":{"date-parts":[[2022,5,16]]}}}