{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T20:07:55Z","timestamp":1779739675885,"version":"3.53.1"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"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":["62366004"],"award-info":[{"award-number":["62366004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018571","name":"Specific Research Project of Guangxi for Research Bases and Talents","doi-asserted-by":"publisher","award":["AD25069071"],"award-info":[{"award-number":["AD25069071"]}],"id":[{"id":"10.13039\/501100018571","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017691","name":"Guangxi Key Research and Development Program","doi-asserted-by":"publisher","award":["AB24010316"],"award-info":[{"award-number":["AB24010316"]}],"id":[{"id":"10.13039\/501100017691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015794","name":"Innovation Project of Guangxi Graduate Education","doi-asserted-by":"publisher","award":["YCSW2025100"],"award-info":[{"award-number":["YCSW2025100"]}],"id":[{"id":"10.13039\/501100015794","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2026,6,1]]},"DOI":"10.1109\/jiot.2026.3672515","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T19:54:57Z","timestamp":1773172497000},"page":"24343-24359","source":"Crossref","is-referenced-by-count":0,"title":["FedAFF: Adaptive Feature Filtering for Collaborative Fine-Tuning in Personalized Federated Learning"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7495-9124","authenticated-orcid":false,"given":"Jiali","family":"Zheng","sequence":"first","affiliation":[{"name":"Guangxi Key Laboratory of Multimedia Communications and Network Technology, School of Computer, Electronics and Information, Guangxi University, Nanning, Guangxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6638-5980","authenticated-orcid":false,"given":"Shulin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Multimedia Communications and Network Technology, School of Computer, Electronics and Information, Guangxi University, Nanning, Guangxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengbo","family":"Hu","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Multimedia Communications and Network Technology, School of Computer, Electronics and Information, Guangxi University, Nanning, Guangxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5346-9492","authenticated-orcid":false,"given":"Xiaoliang","family":"Shen","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Multimedia Communications and Network Technology, School of Computer, Electronics and Information, Guangxi University, Nanning, Guangxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2021.01.028"},{"key":"ref2","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3231363"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-022-01647-y"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3262546"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3365142"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2956615"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2994596"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3376548"},{"key":"ref10","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. 3rd Mach. Learn. Syst. Conf.","author":"Li"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16960"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86486-6_36"},{"key":"ref13","article-title":"Personalized federated learning with Moreau envelopes","author":"Dinh","year":"2020","journal-title":"arXiv:2006.08848"},{"key":"ref14","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.","volume":"33","author":"Fallah"},{"key":"ref15","article-title":"Federated meta-learning with fast convergence and efficient communication","author":"Chen","year":"2018","journal-title":"arXiv:1802.07876"},{"key":"ref16","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref17","article-title":"Federated learning with personalization layers","author":"Ghuhan Arivazhagan","year":"2019","journal-title":"arXiv:1912.00818"},{"key":"ref18","article-title":"Exploiting shared representations for personalized federated learning","author":"Collins","year":"2021","journal-title":"arXiv:2102.07078"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599345"},{"key":"ref20","first-page":"1","article-title":"On the convergence of FedAvg on non-IID data","volume-title":"Proc. 8th Int. Conf. Learn. Represent.","author":"Li"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"ref22","article-title":"Federated multi-task learning","author":"Smith","year":"2017","journal-title":"arXiv:1705.10467"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS47774.2020.00111"},{"key":"ref25","first-page":"6010","article-title":"RIFLE: Backpropagation in depth for deep transfer learning through re-initializing the fully-connected LayEr","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","volume":"1","author":"Li"},{"key":"ref26","article-title":"On bridging generic and personalized federated learning for image classification","author":"Chen","year":"2021","journal-title":"arXiv:2107.00778"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3449129"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/SECON55815.2022.9918588"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/MSN60784.2023.00084"},{"key":"ref30","article-title":"Heterogeneous federated learning: State-of-the-art and research challenges","author":"Ye","year":"2023","journal-title":"arXiv:2307.10616"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref33","first-page":"9489","article-title":"Personalized federated learning using hypernetworks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Shamsian"},{"key":"ref34","article-title":"FedBABU: Towards enhanced representation for federated image classification","author":"Oh","year":"2021","journal-title":"arXiv:2106.06042"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"ref38","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017","journal-title":"arXiv:1708.07747"},{"key":"ref39","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref40","article-title":"A downsampled variant of ImageNet as an alternative to the CIFAR datasets","author":"Chrabaszcz","year":"2017","journal-title":"arXiv:1707.08819"},{"key":"ref41","first-page":"649","article-title":"Character-level convolutional networks for text classification","volume-title":"Proc. NIPS","author":"Zhang"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/301"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611781"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01139"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00170"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/11534158\/11428226.pdf?arnumber=11428226","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T19:56:36Z","timestamp":1779738996000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11428226\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,1]]},"references-count":45,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2026.3672515","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,1]]}}}