{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:45:13Z","timestamp":1775324713354,"version":"3.50.1"},"reference-count":34,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Hong Kong General Research Fund","award":["16203319"],"award-info":[{"award-number":["16203319"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872417"],"award-info":[{"award-number":["61872417"]}],"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":["62061160490"],"award-info":[{"award-number":["62061160490"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Biomed. Health Inform."],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1109\/jbhi.2022.3198440","type":"journal-article","created":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T19:28:07Z","timestamp":1660937287000},"page":"5596-5607","source":"Crossref","is-referenced-by-count":48,"title":["Customized Federated Learning for Multi-Source Decentralized Medical Image Classification"],"prefix":"10.1109","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8275-0996","authenticated-orcid":false,"given":"Jeffry","family":"Wicaksana","sequence":"first","affiliation":[{"name":"Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2039-3863","authenticated-orcid":false,"given":"Zengqiang","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6252-1061","authenticated-orcid":false,"given":"Xin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3800-3533","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"}]},{"given":"Lixin","family":"Fan","sequence":"additional","affiliation":[{"name":"WeBank, AI Lab, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3885-4912","authenticated-orcid":false,"given":"Kwang-Ting","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Kowloon, Hong Kong"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00847"},{"key":"ref32","first-page":"506","article-title":"Learning multiple visual domains with residual adapters","author":"rebuffi","year":"0","journal-title":"Proc 31st Int Neural Inf Process Syst"},{"key":"ref31","article-title":"Salvaging federated learning by local adaptation","author":"yu","year":"2020"},{"key":"ref30","article-title":"Federated learning of a mixture of global and local models","author":"hanzely","year":"2020"},{"key":"ref34","first-page":"2790","article-title":"Parameter-efficient transfer learning for NLP","author":"houlsby","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref10","article-title":"Private federated learning with domain adaptation","author":"peterson","year":"2018"},{"key":"ref11","article-title":"Split learning for health: Distributed deep learning without sharing raw patient data","author":"vepakomma","year":"2019"},{"key":"ref12","article-title":"Adaptive personalized federated learning","author":"deng","year":"2020"},{"key":"ref13","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","author":"fallah","year":"0","journal-title":"Proc Conf Workshop Neural Inf Process Syst"},{"key":"ref14","first-page":"3320","article-title":"How transferable are features in deep neural networks?","author":"yosinski","year":"0","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2303821"},{"key":"ref16","article-title":"Prostatex challenge data. the cancer imaging archive","author":"geert","year":"2017"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-013-9622-7"},{"key":"ref18","article-title":"The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions","volume":"5","author":"tschandl","year":"2018","journal-title":"Data Science Journal"},{"key":"ref19","article-title":"Federated learning with non-iid data","author":"zhao","year":"2018"},{"key":"ref28","first-page":"21394","article-title":"Personalized federated learning with moreau envelopes","author":"dinh","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3002985"},{"key":"ref27","first-page":"429","article-title":"Federated optimization in heterogeneous networks","author":"li","year":"0","journal-title":"Proc Mach Learn Syst"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00107"},{"key":"ref6","article-title":"Three approaches for personalization with applications to federated learning","author":"mansour","year":"2020"},{"key":"ref29","first-page":"2304","article-title":"Lower bounds and optimal algorithms for personalized federated learning","author":"hanzely","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2872813"},{"key":"ref8","article-title":"FedMD: Heterogeneous federated learning via model distillation","author":"li","year":"2019"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.2988604"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101765"},{"key":"ref9","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","author":"lin","year":"0","journal-title":"Proc Conf Workshop Neural Inf Process Syst"},{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"0","journal-title":"Proc Artif Intell Statist"},{"key":"ref20","article-title":"Towards understanding ensemble, knowledge distillation and self-distillation in deep learning","author":"allen-zhu","year":"2020"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3040015"},{"key":"ref21","article-title":"Personalized federated learning with first order model optimization","author":"zhang","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC44109.2020.9175344"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref25","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","author":"karimireddy","year":"0","journal-title":"Proc Int Conf Mach Learn"}],"container-title":["IEEE Journal of Biomedical and Health Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221020\/9945616\/09855868.pdf?arnumber=9855868","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T19:35:10Z","timestamp":1670873710000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9855868\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11]]},"references-count":34,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/jbhi.2022.3198440","relation":{},"ISSN":["2168-2194","2168-2208"],"issn-type":[{"value":"2168-2194","type":"print"},{"value":"2168-2208","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11]]}}}