{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T16:00:24Z","timestamp":1788278424078,"version":"build-2803163510"},"reference-count":48,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"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":["62076129"],"award-info":[{"award-number":["62076129"]}],"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":["62136004"],"award-info":[{"award-number":["62136004"]}],"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":["61501230"],"award-info":[{"award-number":["61501230"]}],"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":["62272226"],"award-info":[{"award-number":["62272226"]}],"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":["61732006"],"award-info":[{"award-number":["61732006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Plan of Jiangsu Province","award":["BE2022842"],"award-info":[{"award-number":["BE2022842"]}]},{"name":"National Key R&amp;D Program of China","award":["2018YFC2001600"],"award-info":[{"award-number":["2018YFC2001600"]}]},{"name":"National Key R&amp;D Program of China","award":["2018YFC2001602"],"award-info":[{"award-number":["2018YFC2001602"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Big Data"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1109\/tbdata.2023.3264109","type":"journal-article","created":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T17:37:12Z","timestamp":1680543432000},"page":"1334-1346","source":"Crossref","is-referenced-by-count":19,"title":["Self-Supervised Federated Adaptation for Multi-Site Brain Disease Diagnosis"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5709-6798","authenticated-orcid":false,"given":"Qiming","family":"Yang","sequence":"first","affiliation":[{"name":"MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7740-292X","authenticated-orcid":false,"given":"Qi","family":"Zhu","sequence":"additional","affiliation":[{"name":"MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3736-9051","authenticated-orcid":false,"given":"Mingming","family":"Wang","sequence":"additional","affiliation":[{"name":"MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8554-7245","authenticated-orcid":false,"given":"Wei","family":"Shao","sequence":"additional","affiliation":[{"name":"MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1470-6998","authenticated-orcid":false,"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5658-7643","authenticated-orcid":false,"given":"Daoqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1377\/hlthaff.2014.0147"},{"key":"ref35","first-page":"4182","article-title":"Data-efficient image recognition with contrastive predictive coding","author":"henaff","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref12","article-title":"Domain adaptation for medical image analysis: A survey","author":"guan","year":"2021"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00674"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3188292"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.4324\/9780429031625"},{"key":"ref36","article-title":"On the convergence of FedAvg on Non-IID data","author":"li","year":"2019"},{"key":"ref31","article-title":"FedBE: Making Bayesian model ensemble applicable to federated learning","author":"chen","year":"2020"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI48211.2021.9433971"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488502001648"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2987817"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-79228-4_1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102063"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2017.2734883"},{"key":"ref17","article-title":"Federated learning based on dynamic regularization","author":"acar","year":"2021"},{"key":"ref39","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","author":"long","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref16","article-title":"Federated learning via posterior averaging: A new perspective and practical algorithms","author":"al-shedivat","year":"2020"},{"key":"ref38","first-page":"315","article-title":"Deep sparse rectifier neural networks","author":"glorot","year":"2011","journal-title":"Proc 14th Int Conf Artif Intell Statist"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449847"},{"key":"ref18","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Proc Artif Intell Statist"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"ref46","article-title":"KD3A: Unsupervised multi-source decentralized domain adaptation via knowledge distillation","author":"feng","year":"2020"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134012"},{"key":"ref45","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"li","year":"2020","journal-title":"Proc Mach Learn Syst"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.2988575"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2021.06.005"},{"key":"ref25","first-page":"14774","article-title":"Deep leakage from gradients","author":"zhu","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101765"},{"key":"ref20","article-title":"A public domain dataset for human activity recognition using smartphones","volume":"3","author":"anguita","year":"2013","journal-title":"ESANN"},{"key":"ref42","article-title":"Domain-invariant adversarial learning for unsupervised domain adaption","author":"zhang","year":"2018"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1038\/mp.2013.78"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2009.2032660"},{"key":"ref44","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref21","article-title":"LoAdaBoost: Loss-based AdaBoost federated machine learning on medical data","author":"huang","year":"2018"},{"key":"ref43","article-title":"FedBN: Federated learning on non-iid features via local batch normalization","author":"li","year":"2021"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59713-9_43"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2933160"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/s12021-016-9318-5"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.10.045"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.12.052"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2017.08.017"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3077079"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101755"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.03.027"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065720500124"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2864732"}],"container-title":["IEEE Transactions on Big Data"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6687317\/10236926\/10091149.pdf?arnumber=10091149","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T18:36:06Z","timestamp":1695666966000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10091149\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10]]},"references-count":48,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tbdata.2023.3264109","relation":{},"ISSN":["2332-7790","2372-2096"],"issn-type":[{"value":"2332-7790","type":"electronic"},{"value":"2372-2096","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10]]}}}