{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T20:59:34Z","timestamp":1766437174700,"version":"3.48.0"},"reference-count":38,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"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":["62202247"],"award-info":[{"award-number":["62202247"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Consumer Electron."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1109\/tce.2025.3624693","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T17:51:24Z","timestamp":1761241884000},"page":"11440-11452","source":"Crossref","is-referenced-by-count":0,"title":["Fed-HealthGen: A Generative Federated Framework for Privacy-Preserving Personalized Healthcare Using Wearable Consumer Electronics"],"prefix":"10.1109","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0884-6601","authenticated-orcid":false,"given":"Jianhui","family":"Lv","sequence":"first","affiliation":[{"name":"Multi-Modal Data Fusion and Precision Medicine Laboratory, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6555-3464","authenticated-orcid":false,"given":"Byung-Gyu","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Artificial Intelligence Engineering, Sookmyung Women’s University, Seoul, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5224-4048","authenticated-orcid":false,"given":"Keqin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science, State University of New York, New Paltz, NY, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3358803"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2018.00743"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3371928"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-019-0212-z"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2023.9010049"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.26599\/TST.2024.9010122"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102780"},{"key":"ref9","first-page":"4387","article-title":"The non-iid data quagmire of decentralized machine learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hsieh"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2765202"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3460469"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3650215.3650298"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref14","first-page":"1","article-title":"Federated learning with matched averaging","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Wang"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0268-3"},{"key":"ref16","first-page":"301","article-title":"Doctor AI: Predicting clinical events via recurrent neural networks","volume-title":"Proc. Mach. Learn. Healthcare Conf.","author":"Choi"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.14744\/AnatolJCardiol.2019.28661"},{"key":"ref18","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":"ref19","first-page":"1","article-title":"Breaking interprovincial data silos: How federated learning can unlock Canada\u2019s public health potential","volume":"5","author":"Yazdinejad","year":"2025","journal-title":"Available SSRN"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2024.3459789"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3371501"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3420126"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2988604"},{"key":"ref24","article-title":"Federated learning with personalization layers","author":"Arivazhagan","year":"2019","journal-title":"arXiv:1912.00818"},{"key":"ref25","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li","year":"2018","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/WorldS450073.2020.9210355"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TCE.2024.3377550"},{"key":"ref28","first-page":"5508","article-title":"Time-series generative adversarial networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Yoon"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-42516-z"},{"key":"ref30","article-title":"FedGAN: Federated generative adversarial networks for distributed data","author":"Rasouli","year":"2020","journal-title":"arXiv:2006.07228"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2024789118"},{"key":"ref32","first-page":"6453","article-title":"Federated principal component analysis","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Grammenos"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/DSIT61374.2024.10880906"},{"key":"ref34","first-page":"21394","article-title":"Personalized federated learning with Moreau envelopes","volume-title":"Proc. NIPS","author":"Dinh"},{"key":"ref35","article-title":"FedBN: Federated learning on non-IID features via local batch normalization","author":"Li","year":"2021","journal-title":"arXiv:2102.07623"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126831"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86486-6_36"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.3390\/electronics13050857"}],"container-title":["IEEE Transactions on Consumer Electronics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/30\/11306167\/11215718.pdf?arnumber=11215718","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T18:43:00Z","timestamp":1766428980000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11215718\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":38,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tce.2025.3624693","relation":{},"ISSN":["0098-3063","1558-4127"],"issn-type":[{"type":"print","value":"0098-3063"},{"type":"electronic","value":"1558-4127"}],"subject":[],"published":{"date-parts":[[2025,11]]}}}