{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T18:06:50Z","timestamp":1769278010287,"version":"3.49.0"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2020B010165003"],"award-info":[{"award-number":["2020B010165003"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62006252"],"award-info":[{"award-number":["62006252"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176269"],"award-info":[{"award-number":["62176269"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2021A1515011840"],"award-info":[{"award-number":["2021A1515011840"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Parallel Distrib. Syst."],"published-print":{"date-parts":[[2022,12,1]]},"DOI":"10.1109\/tpds.2022.3168873","type":"journal-article","created":{"date-parts":[[2022,4,22]],"date-time":"2022-04-22T19:32:25Z","timestamp":1650655945000},"page":"3718-3731","source":"Crossref","is-referenced-by-count":12,"title":["Distributed Evolution Strategies for Black-Box Stochastic Optimization"],"prefix":"10.1109","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4460-2460","authenticated-orcid":false,"given":"Xiaoyu","family":"He","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7872-7718","authenticated-orcid":false,"given":"Zibin","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7048-3445","authenticated-orcid":false,"given":"Chuan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0497-0835","authenticated-orcid":false,"given":"Yuren","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"}]},{"given":"Chuan","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Software, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2559-2383","authenticated-orcid":false,"given":"Qingwei","family":"Lin","sequence":"additional","affiliation":[{"name":"Microsoft Research, Beijing, China"}]}],"member":"263","reference":[{"key":"ref39","first-page":"1613","article-title":"Online to offline conversions, universality and adaptive minibatch sizes","author":"levy","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref38","article-title":"On the convergence of adam and beyond","author":"reddi","year":"2018","journal-title":"Proc 6th Int Conf Learn Representations"},{"key":"ref33","article-title":"Cooperative SGD: A unified framework for the design and analysis of communication-efficient SGD algorithms","author":"wang","year":"2019"},{"key":"ref32","article-title":"Parallel SGD: When does averaging help?","author":"zhang","year":"2016"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/447"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/2725494.2725500"},{"key":"ref37","first-page":"6677","article-title":"AdaGrad stepsizes: Sharp convergence over nonconvex landscapes","author":"ward","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref36","first-page":"2737","article-title":"Asynchronous parallel stochastic gradient for nonconvex optimization","author":"lian","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref34","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Proc 20th Int Conf Artif Intell Statist"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2020.100694"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-43505-2_44"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1137\/140984038"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1137\/16M1080173"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729586"},{"key":"ref20","article-title":"signSGD via zeroth-order oracle","author":"liu","year":"2019","journal-title":"Proc 7th Int Conf Learn Representations"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2890140"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3012609"},{"key":"ref24","author":"nocedal","year":"2006","journal-title":"Numer Optim"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2015.2409256"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1023\/A:1015059928466"},{"key":"ref25","first-page":"560","article-title":"signSGD: Compressed optimisation for non-convex problems","author":"bernstein","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3205455.3205606"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2011.2182178"},{"key":"ref55","first-page":"3252","article-title":"Error feedback fixes SignSGD and other gradient compression schemes","author":"karimireddy","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/s101070100263"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2009.2027527"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1137\/120880811"},{"key":"ref10","article-title":"Federated optimization: Distributed machine learning for on-device intelligence","author":"kone?n\u00fd","year":"2016"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2019.1800286"},{"key":"ref40","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"duchi","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref12","first-page":"1","article-title":"Automatic differentiation in machine learning: A survey","volume":"18","author":"baydin","year":"2018","journal-title":"J Mach Learn Res"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2016.2550005"},{"key":"ref14","first-page":"129:1","article-title":"Time-to-event prediction with neural networks and cox regression.","volume":"20","author":"kvamme","year":"2019","journal-title":"J Mach Learn Res"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s10208-015-9296-2"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s10898-014-0174-2"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1002\/rnc.3164"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2480419"},{"key":"ref19","first-page":"1812","article-title":"Faster derivative-free stochastic algorithm for shared memory machines","author":"gu","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2015.2496908"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2950779"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2020.08.045"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2976000"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2020.3023660"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.3003539"},{"key":"ref49","first-page":"86:1","article-title":"Parallel genetic algorithms: A useful survey","volume":"53","author":"harada","year":"2020","journal-title":"ACM Comput Surv"},{"key":"ref9","article-title":"Federated learning: Strategies for improving communication efficiency","author":"kone?n\u00fd","year":"2016"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2018.05.016"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2017.2668068"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2015.04.061"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1162\/evco_a_00201"},{"key":"ref42","article-title":"Local AdaAlter: Communication-efficient stochastic gradient descent with adaptive learning rates","author":"xie","year":"2020","journal-title":"Proc 12th Annu Workshop Optim Mach Learn"},{"key":"ref41","article-title":"Adaptive federated optimization","author":"reddi","year":"2020"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2016.09.002"},{"key":"ref43","article-title":"Effective federated adaptive gradient methods with non-IID decentralized data","author":"tong","year":"2020"}],"container-title":["IEEE Transactions on Parallel and Distributed Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/71\/9790018\/09762038.pdf?arnumber=9762038","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,22]],"date-time":"2022-08-22T19:50:03Z","timestamp":1661197803000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9762038\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,1]]},"references-count":55,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tpds.2022.3168873","relation":{},"ISSN":["1045-9219","1558-2183","2161-9883"],"issn-type":[{"value":"1045-9219","type":"print"},{"value":"1558-2183","type":"electronic"},{"value":"2161-9883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,1]]}}}