{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:37:22Z","timestamp":1783183042501,"version":"3.54.6"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N000142212305"],"award-info":[{"award-number":["N000142212305"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-23-C-1016"],"award-info":[{"award-number":["N00014-23-C-1016"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-2146171"],"award-info":[{"award-number":["CNS-2146171"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-2212565"],"award-info":[{"award-number":["CNS-2212565"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Cogn. Commun. Netw."],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1109\/tccn.2024.3352976","type":"journal-article","created":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T18:24:40Z","timestamp":1704997480000},"page":"1011-1025","source":"Crossref","is-referenced-by-count":8,"title":["Multi-Source to Multi-Target Decentralized Federated Domain Adaptation"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7550-1120","authenticated-orcid":false,"given":"Su","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4266-4000","authenticated-orcid":false,"given":"Seyyedali","family":"Hosseinalipour","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, University at Buffalo–SUNY, Buffalo, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2771-3521","authenticated-orcid":false,"given":"Christopher G.","family":"Brinton","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Federated learning: Strategies for improving communication efficiency","author":"Kone\u010dn\u1ef3","year":"2017","journal-title":"arXiv:1610.05492"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"ref3","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488679"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2022.3152445"},{"key":"ref6","article-title":"Braintorrent: A peer-to-peer environment for decentralized federated learning","author":"Roy","year":"2019","journal-title":"arXiv:1905.06731"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488906"},{"key":"ref8","first-page":"1","article-title":"Federated adversarial domain adaptation","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Peng"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3084358"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref11","first-page":"1","article-title":"Domain adaptation with multiple sources","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Mansour"},{"key":"ref12","first-page":"1","article-title":"Adversarial multiple source domain adaptation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhao"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3012756"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488705"},{"key":"ref16","first-page":"1","article-title":"Federated multi-task learning","volume-title":"Proc. 31st Adv. Neural Inf. Process. Syst.","author":"Smith"},{"key":"ref17","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Fallah"},{"key":"ref18","first-page":"21394","article-title":"Personalized federated learning with moreau envelopes","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dinh"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2022.3216326"},{"key":"ref20","article-title":"Federated learning with non-IID data","author":"Zhao","year":"2022","journal-title":"arXiv:1806.00582"},{"key":"ref21","first-page":"1","article-title":"Federated learning from only unlabeled data with class-conditional-sharing clients","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Lu"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.005.2200925"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICC45041.2023.10279348"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488818"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2021.3075432"},{"key":"ref27","first-page":"425","article-title":"Federated learning under arbitrary communication patterns","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Avdiukhin"},{"key":"ref28","article-title":"Parallel successive learning for dynamic distributed model training over heterogeneous wireless networks","author":"Hosseinalipour","year":"2023","journal-title":"arXiv:2202.02947"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.609"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.05.083"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00814"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946704"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"ref34","first-page":"7523","article-title":"On learning invariant representations for domain adaptation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhao"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3234977"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/tetc.2024.3356068"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00115"},{"key":"ref38","first-page":"463","article-title":"Rademacher and gaussian complexities: Risk bounds and structural results","volume":"3","author":"Bartlett","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737464"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2777480"},{"key":"ref41","first-page":"1","article-title":"Integrated anchor and social link predictions across social networks","volume-title":"Proc. 24th Int. Joint Conf. Artif. Intell.","author":"Zhang"},{"issue":"83","key":"ref42","first-page":"1","article-title":"CVXPY: A python-embedded modeling language for convex optimization","volume":"17","author":"Diamond","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1561\/9781933019574"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2013.10.016"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1512\/iumj.1973.22.22045"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/34.291440"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.161"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2021.3106104"},{"key":"ref50","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. 33rd Adv. Neural Inf. Process. Syst.","author":"Paszke"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2017.2751045"},{"key":"ref52","first-page":"1","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ganin"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107298019"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/s11081-007-9001-7"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2007.05960"}],"container-title":["IEEE Transactions on Cognitive Communications and Networking"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/6687307\/10551670\/10391064-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6687307\/10551670\/10391064.pdf?arnumber=10391064","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T20:54:02Z","timestamp":1719348842000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10391064\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":55,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tccn.2024.3352976","relation":{},"ISSN":["2332-7731","2372-2045"],"issn-type":[{"value":"2332-7731","type":"electronic"},{"value":"2372-2045","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6]]}}}