{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T10:47:34Z","timestamp":1780051654642,"version":"3.53.1"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T00:00:00Z","timestamp":1685577600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172319"],"award-info":[{"award-number":["62172319"]}],"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":["U19B200073"],"award-info":[{"award-number":["U19B200073"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Parallel Distrib. Syst."],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1109\/tpds.2023.3267897","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T18:15:21Z","timestamp":1681755321000},"page":"1923-1941","source":"Crossref","is-referenced-by-count":24,"title":["Dap-FL: Federated Learning Flourishes by Adaptive Tuning and Secure Aggregation"],"prefix":"10.1109","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6956-8185","authenticated-orcid":false,"given":"Qian","family":"Chen","sequence":"first","affiliation":[{"name":"State Key Laboratory of Integrated Service Networks, School of Cyber Engineering, Xidian University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1525-3356","authenticated-orcid":false,"given":"Zilong","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Networks, School of Cyber Engineering, Xidian University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3244-3418","authenticated-orcid":false,"given":"Jiawei","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Networks, School of Cyber Engineering, Xidian University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1784-6091","authenticated-orcid":false,"given":"Haonan","family":"Yan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Integrated Service Networks, School of Cyber Engineering, Xidian University, Xi&#x2019;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8916-6645","authenticated-orcid":false,"given":"Xiaodong","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Guelph, Guelph, ON, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaa8415"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2014.2300753"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2016.2603219"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.18391"},{"key":"ref7","first-page":"323","article-title":"Global analytics in the face of bandwidth and regulatory constraints","volume-title":"Proc. 12th USENIX Symp. Netw. Syst. Des. Implementation","author":"Vulimiri"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57959-7"},{"key":"ref9","first-page":"1","article-title":"Federated learning: Strategies for improving communication efficiency","volume-title":"Proc. NIPS Workshop Private Multi-Party Mach. Learn.","author":"Kone\u010dn\u00fd"},{"key":"ref10","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.29012\/jpc.v4i2.623"},{"key":"ref13","first-page":"374","article-title":"Towards federated learning at scale: System design","volume-title":"Proc. 2nd SysML Conf.","author":"Bonawitz"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2018.01.007"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3369583.3392686"},{"key":"ref16","first-page":"1223","article-title":"Large scale distributed deep networks","volume-title":"Proc. 26th Int. Conf. Neural Inf. Process. Syst.","author":"Dean"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3095077"},{"key":"ref18","first-page":"17455","article-title":"Differentially private learning with adaptive clipping","volume-title":"Proc. 35th Int. Conf. Neural Inf. Process. Syst.","author":"Andrew"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/IEEECONF44664.2019.9049066"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-377-6.50045-1"},{"key":"ref21","first-page":"2546","article-title":"Algorithms for hyper-parameter optimization","volume-title":"Proc. 25th Int. Conf. Neural Inf. Process. Syst.","author":"Bergstra"},{"key":"ref22","first-page":"2171","article-title":"Scalable Bayesian optimization using deep neural networks","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","author":"Snoek"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488679"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2021.3084406"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3166101"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9149138"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737464"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3034674"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3095506"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/WF-IoT48130.2020.9221089"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3064351"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2973651"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2945367"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/998929476"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1017\/cbo9780511721656"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1613\/jair.301"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1512\/iumj.1957.6.56038"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2743240"},{"key":"ref42","first-page":"1","article-title":"Continuous control with deep reinforcement learning","volume-title":"Proc. 4th Int. Conf. Learn. Representations","author":"Lillicrap"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-48910-X_16"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8486403"},{"key":"ref45","first-page":"1","article-title":"On the convergence of FedAvg on non-IID data","volume-title":"Proc. 8th Int. Conf. Learn. Representations","author":"Li"},{"key":"ref46","volume-title":"Constrained Markov Decision Processes","author":"Altman","year":"1999"},{"key":"ref47","first-page":"165","article-title":"Solving very large weakly coupled Markov decision processes","volume-title":"Proc. 15th Nat. Conf. Artif. Intell.","author":"Meuleau"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-0348-0439-4_14"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/168588.168596"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102966"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3024629"},{"key":"ref53","first-page":"2961","article-title":"Actor-attention-critic for multi-agent reinforcement learning","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Iqbal"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"}],"container-title":["IEEE Transactions on Parallel and Distributed Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/71\/10123122\/10103633.pdf?arnumber=10103633","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T02:37:47Z","timestamp":1709260667000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10103633\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6]]},"references-count":53,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tpds.2023.3267897","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":[[2023,6]]}}}