{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T18:52:47Z","timestamp":1774896767635,"version":"3.50.1"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"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":["62172003"],"award-info":[{"award-number":["62172003"]}],"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":["62441207"],"award-info":[{"award-number":["62441207"]}],"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":["62402029"],"award-info":[{"award-number":["62402029"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Natural Science Foundation","award":["L251041"],"award-info":[{"award-number":["L251041"]}]},{"name":"Seatrium New Energy Laboratory"},{"name":"Singapore Ministry of Education Tier 1","award":["RT5\/23"],"award-info":[{"award-number":["RT5\/23"]}]},{"name":"Singapore Ministry of Education Tier 1","award":["RG24\/24"],"award-info":[{"award-number":["RG24\/24"]}]},{"name":"NTU Centre for Computational Technologies in Finance"},{"name":"A*STAR-administered IAF-ICP program","award":["I2301E0026"],"award-info":[{"award-number":["I2301E0026"]}]},{"name":"Ministry of Science and ICT, Korea","award":["IITP-2020-0-01821"],"award-info":[{"award-number":["IITP-2020-0-01821"]}]},{"name":"Institute for ICT Planning and Evaluation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Mobile Comput."],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1109\/tmc.2025.3618886","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T17:54:03Z","timestamp":1759859643000},"page":"3568-3582","source":"Crossref","is-referenced-by-count":2,"title":["Mitigating Catastrophic Forgetting in Personalized Federated Learning for Edge Devices Using State-Space Models"],"prefix":"10.1109","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1324-3960","authenticated-orcid":false,"given":"Weidong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Anhui University of Technology, and Anhui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry, Anhui, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2954-1650","authenticated-orcid":false,"given":"Dongshang","family":"Deng","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7912-8757","authenticated-orcid":false,"given":"Xuangou","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University of Technology, and Anhui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry, Anhui, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3366-7640","authenticated-orcid":false,"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Cyberspace Science and Technology, Beijing Jiaotong University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7565-7072","authenticated-orcid":false,"given":"Xiao","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Anhui University of Technology, and Anhui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry, Anhui, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7442-7416","authenticated-orcid":false,"given":"Dusit","family":"Niyato","sequence":"additional","affiliation":[{"name":"College of Computing and Data Science, Nanyang Technological University, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7711-8072","authenticated-orcid":false,"given":"Dong In","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Sungkyunkwan University, Jongno-gu, South Korea"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3708495"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3507286"},{"key":"ref3","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","volume":"54","author":"McMahan","year":"2017"},{"key":"ref4","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"139","author":"Li","year":"2021"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3498346"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02478"},{"key":"ref8","first-page":"1","article-title":"Federated orthogonal training: Mitigating global catastrophic forgetting in continual federated learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Bakman","year":"2024"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01177"},{"key":"ref10","first-page":"1","article-title":"Towards understanding and mitigating dimensional collapse in heterogeneous federated learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Shi","year":"2023"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3297103"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00170"},{"key":"ref13","first-page":"1","article-title":"Personalized federated learning with first order model optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang","year":"2021"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26330"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"ref16","first-page":"21394","article-title":"Personalized federated learning with moreau envelopes","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Dinh","year":"2020"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3446271"},{"key":"ref18","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. MLSys","volume":"2","author":"Li","year":"2020"},{"key":"ref19","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"119","author":"Karimireddy","year":"2020"},{"key":"ref20","first-page":"1","article-title":"Personalized federated learning with feature alignment and classifier collaboration","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu","year":"2023"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00987"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00980"},{"key":"ref23","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","year":"2020"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2023.3330910"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3558005"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01955"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29763-x"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00993"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i15.29562"},{"key":"ref30","article-title":"Federated learning with personalization layers","author":"Arivazhagan","year":"2019"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2022.3231527"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20050-2_41"},{"key":"ref33","first-page":"1","article-title":"Acceleration of federated learning with alleviated forgetting in local training","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu","year":"2022"},{"key":"ref34","first-page":"1","article-title":"Accurate forgetting for heterogeneous federated continual learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wuerkaixi","year":"2024"},{"key":"ref35","first-page":"66408","article-title":"A data-free approach to mitigate catastrophic forgetting in federated class incremental learning for vision tasks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Babakniya","year":"2023"},{"key":"ref36","article-title":"Adaptive personalized federated learning","author":"Deng","year":"2020"},{"key":"ref37","first-page":"1","article-title":"Heterogeneous personalized federated learning by local-global updates mixing via convergence rate","volume-title":"Proc. 12th Int. Conf. Learn. Representations","author":"Jiang","year":"2024"},{"key":"ref38","article-title":"Federated learning of a mixture of global and local models","author":"Hanzely","year":"2021"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3331690"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3264740"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.2985917"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10229027"},{"key":"ref43","article-title":"Fashion-mnist: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"key":"ref44","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-023-02662-6"}],"container-title":["IEEE Transactions on Mobile Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7755\/11372515\/11195747.pdf?arnumber=11195747","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T21:10:37Z","timestamp":1770671437000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11195747\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":45,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tmc.2025.3618886","relation":{},"ISSN":["1536-1233","1558-0660","2161-9875"],"issn-type":[{"value":"1536-1233","type":"print"},{"value":"1558-0660","type":"electronic"},{"value":"2161-9875","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]}}}