{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T04:18:38Z","timestamp":1773980318943,"version":"3.50.1"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Beijing Information Science and Technology University Qin Xin Talents Cultivation Program","award":["QXTCPC202112"],"award-info":[{"award-number":["QXTCPC202112"]}]},{"name":"Research Level Improvement Project","award":["2020KYNH214"],"award-info":[{"award-number":["2020KYNH214"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62401246"],"award-info":[{"award-number":["62401246"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Emerg. Top. Comput. Intell."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1109\/tetci.2025.3572126","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T13:58:41Z","timestamp":1749045521000},"page":"3910-3921","source":"Crossref","is-referenced-by-count":5,"title":["FedCGP: Cluster-Based Gradual Personalization for Federated Medical Image Segmentation"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1004-3613","authenticated-orcid":false,"given":"Ke","family":"Niu","sequence":"first","affiliation":[{"name":"Computer School, Beijing Information Science and Technology University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1079-1195","authenticated-orcid":false,"given":"Wenjuan","family":"Tai","sequence":"additional","affiliation":[{"name":"Computer School, Beijing Information Science and Technology University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9746-5202","authenticated-orcid":false,"given":"Jiuyun","family":"Cai","sequence":"additional","affiliation":[{"name":"Computer School, Beijing Information Science and Technology University, Beijing, China"}]},{"given":"Yuhang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Computer School, Beijing Information Science and Technology University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7754-7141","authenticated-orcid":false,"given":"Heng","family":"Li","sequence":"additional","affiliation":[{"name":"Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"McMahan","year":"2017"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3260027"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-023-01994-5"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3501296"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2023.3245103"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3625558"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3250275"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3594806.3596568"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122874"},{"key":"ref10","first-page":"1","article-title":"FedBN: Federated learning on non-IID features via local batch normalization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li","year":"2021"},{"key":"ref11","article-title":"Federated learning with personalization layers","author":"Arivazhagan","year":"2019"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/tit.2022.3192506"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-022-01046-x"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3015958"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16960"},{"key":"ref16","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume-title":"Proc. Adv. neural inf. proces. syst.","volume":"33","author":"Jiang","year":"2020"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.02.021"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/SIU59756.2023.10223935"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s10844-023-00797-x"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412599"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9149323"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD46524.2019.00038"},{"key":"ref23","article-title":"XOR mixup: Privacy-preserving data augmentation for one-shot federated learning","author":"Hwang","year":"2020"},{"key":"ref24","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","author":"Li","year":"2020"},{"key":"ref25","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","author":"Karimireddy","year":"2019"},{"key":"ref26","first-page":"21394","article-title":"Personalized federated learning with moreau envelopes","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"T Dinh","year":"2020"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19803-8_27"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00985"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3233405"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2024.108141"},{"key":"ref32","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","year":"2020"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3263072"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00042"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-00234-2"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106294"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3299206"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2377694"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.117"},{"key":"ref41","article-title":"Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge","author":"Bakas","year":"2018"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2004.825627"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1161\/ATVBAHA.111.225219"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3163352"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/42.845178"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01564-3"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2587062"}],"container-title":["IEEE Transactions on Emerging Topics in Computational Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7433297\/11267152\/11023212.pdf?arnumber=11023212","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T05:51:20Z","timestamp":1764049880000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11023212\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":47,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tetci.2025.3572126","relation":{},"ISSN":["2471-285X"],"issn-type":[{"value":"2471-285X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12]]}}}