{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T18:17:36Z","timestamp":1782152256653,"version":"3.54.5"},"reference-count":272,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFC3307900"],"award-info":[{"award-number":["2024YFC3307900"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376103"],"award-info":[{"award-number":["62376103"]}],"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":["62302184"],"award-info":[{"award-number":["62302184"]}],"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":["62436003"],"award-info":[{"award-number":["62436003"]}],"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":["62206102"],"award-info":[{"award-number":["62206102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Science and Technology Project of Hubei Province","award":["2024BAA008"],"award-info":[{"award-number":["2024BAA008"]}]},{"name":"Hubei Science and Technology Talent Service Project","award":["2024DJC078"],"award-info":[{"award-number":["2024DJC078"]}]},{"DOI":"10.13039\/100014862","name":"Ant Group through CCF-Ant Research Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014862","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Commun. Surv. Tutorials"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/comst.2025.3568279","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T14:03:48Z","timestamp":1746799428000},"page":"1059-1098","source":"Crossref","is-referenced-by-count":14,"title":["Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey"],"prefix":"10.1109","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8630-2504","authenticated-orcid":false,"given":"Yichen","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7591-5315","authenticated-orcid":false,"given":"Haozhao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0983-387X","authenticated-orcid":false,"given":"Wenchao","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianzhe","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6100-6291","authenticated-orcid":false,"given":"Minzhu","family":"Tu","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuying","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0406-6774","authenticated-orcid":false,"given":"Xin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8132-6250","authenticated-orcid":false,"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4485-6743","authenticated-orcid":false,"given":"Shui","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9831-2202","authenticated-orcid":false,"given":"Song","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7791-5511","authenticated-orcid":false,"given":"Ruixuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref2","first-page":"5381","article-title":"A unified theory of decentralized SGD with changing topology and local updates","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Koloskova"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1177\/2631787720977052"},{"key":"ref4","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.001.1900119"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2024.3353265"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2019.2947914"},{"key":"ref8","first-page":"4387","article-title":"The non-IID data quagmire of decentralized machine learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hsieh"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.01.020"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02338"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01955"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3087272"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2016.2579198"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2745201"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3514501"},{"key":"ref17","first-page":"2700","article-title":"Personalized federated recommendation for cold-start users via adaptive knowledge fusion","volume-title":"Proc. Web Conf.","author":"Li"},{"key":"ref18","doi-asserted-by":"crossref","DOI":"10.20944\/preprints202503.1015.v1","article-title":"A systematic survey on federated sequential recommendation","author":"Li","year":"2025"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614834"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2709784"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2894944"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2023.3293650"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1080\/09540099550039318"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/S1364-6613(99)01294-2"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3625558"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.12.036"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106775"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3090430"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3095077"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2023.3315746"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3057446"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00568-3"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3213473"},{"key":"ref36","article-title":"A survey of incremental transfer learning: Combining peer-to-peer federated learning and domain incremental learning for multicenter collaboration","author":"Huang","year":"2023","journal-title":"arXiv:2309.17192"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2022.07.024"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3363240"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01223"},{"key":"ref40","first-page":"12073","article-title":"Federated continual learning with weighted inter-client transfer","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yoon"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/303"},{"key":"ref42","article-title":"Better generative replay for continual federated learning","author":"Qi","year":"2023","journal-title":"arXiv:2302.13001"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/iccv51070.2023.00441"},{"key":"ref44","first-page":"1","article-title":"Accurate forgetting for heterogeneous federated continual learning","volume-title":"Proc. 12th Int. Conf. Learn. Represent.","author":"Wuerkaixi"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2024.3436874"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01218"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2025.3551732"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00383"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3334213"},{"key":"ref50","article-title":"Federated and continual learning for classification tasks in a society of devices","author":"Casado","year":"2020","journal-title":"arXiv:2006.07129"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CIoT57267.2023.10084875"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/3377454"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1002\/wcm.1203"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645416"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3128646"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2022.3151242"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-41589-5_14"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00954"},{"issue":"76","key":"ref59","first-page":"1","article-title":"GADMM: Fast and communication efficient framework for distributed machine learning","volume":"21","author":"Elgabli","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3672042"},{"key":"ref61","first-page":"1","article-title":"FedCDA: Federated learning with cross-rounds divergence-aware aggregation","volume-title":"Proc. 12th Int. Conf. Learn. Represent.","author":"Wang"},{"key":"ref62","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","author":"Li"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref64","first-page":"5132","article-title":"Scaffold: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1145\/3605573.3605584"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118401"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3003744"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2023.3287355"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3222971"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-021-01410-9"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06861-3"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2022.3189051"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2021.10.017"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/9334943"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.3390\/app12020734"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/CCNC49033.2022.9700624"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988195"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-11748-0_2"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3156046"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-01581-6"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.01.012"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.753"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-016-9389-5"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2884462"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"ref86","first-page":"1","article-title":"Gradient episodic memory for continual learning","volume-title":"Proc. 31st Conf. Neural Inf. Process. Syst.","volume":"30","author":"Lopez-Paz"},{"key":"ref87","first-page":"3987","article-title":"Continual learning through synaptic intelligence","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zenke"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"ref89","first-page":"4548","article-title":"Overcoming catastrophic forgetting with hard attention to the task","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Serra"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.naacl-main.398"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-naacl.84"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.410"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00360"},{"key":"ref94","article-title":"Generative replay with feedback connections as a general strategy for continual learning","author":"Van de Ven","year":"2018","journal-title":"arXiv:1809.10635"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-74643-7_35"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/icassp49660.2025.10887681"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72952-2_8"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/CANDAR60563.2023.00009"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/BigData62323.2024.10825220"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73030-6_10"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1145\/3636534.3690701"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72117-5_36"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/ISCC61673.2024.10733586"},{"key":"ref104","article-title":"Exploring the efficacy of federated-continual learning nodes with attention-based classifier for robust Web phishing detection: An empirical investigation","author":"Revathi","year":"2024","journal-title":"arXiv:2405.03537"},{"key":"ref105","article-title":"COALA: A practical and vision-centric federated learning platform","author":"Zhuang","year":"2024","journal-title":"arXiv:2407.16560"},{"key":"ref106","article-title":"Federated continual learning goes online: Leveraging uncertainty for modality-agnostic class-incremental learning","author":"Serra","year":"2024","journal-title":"arXiv:2405.18925"},{"key":"ref107","article-title":"Distributed continual learning","author":"Le","year":"2024","journal-title":"arXiv:2405.17466"},{"key":"ref108","article-title":"Using diffusion models as generative replay in continual federated learning\u2013what will happen?","author":"Mei","year":"2024","journal-title":"arXiv:2411.06618"},{"key":"ref109","first-page":"T2","article-title":"Federated continual learning based on central memory rehearsal","volume":"1","author":"Zhang","year":"2024","journal-title":"Hospital"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111491"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2024.110899"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-96-0963-5_21"},{"key":"ref113","article-title":"Feature aggregation with latent generative replay for federated continual learning of socially appropriate robot behaviours","author":"Churamani","year":"2024","journal-title":"arXiv:2405.15773"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1007\/s11390-025-5186-5"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1109\/SMARTCOMP55677.2022.00027"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1109\/WACV61041.2025.00369"},{"key":"ref117","article-title":"FedMeS: Personalized federated continual learning leveraging local memory","author":"Xie","year":"2024","journal-title":"arXiv:2404.12710"},{"key":"ref118","article-title":"A life-long learning intrusion detection system for 6Genabled IoV","author":"korba","year":"2024","journal-title":"arXiv:2407.15700"},{"key":"ref119","first-page":"350","article-title":"Experience replay for continual learning","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","volume":"32","author":"Rolnick"},{"key":"ref120","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier GANs","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Odena"},{"key":"ref121","article-title":"Federated class-incremental learning: A hybrid approach using latent exemplars and data-free techniques to address local and global forgetting","author":"Nori","year":"2025","journal-title":"arXiv:2501.15356"},{"key":"ref122","article-title":"Exemplar-condensed federated class-incremental learning","author":"Sun","year":"2024","journal-title":"arXiv:2412.18926"},{"key":"ref123","first-page":"1","article-title":"A data-free approach to mitigate catastrophic forgetting in federated class incremental learning for vision tasks","volume-title":"Proc. 37th Int. Conf. Neural Inf. Process. Syst.","author":"Babakniya"},{"key":"ref124","article-title":"Federated class-incremental learning with hierarchical generative prototypes","author":"Salami","year":"2024","journal-title":"arXiv:2406.02447"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3528876"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2025.3542246"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3440029"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3681384"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1109\/ICME57554.2024.10688122"},{"key":"ref130","article-title":"Vertical federated continual learning via evolving prototype knowledge","author":"Wang","year":"2025","journal-title":"arXiv:2502.09152"},{"key":"ref131","article-title":"Addressing spatial-temporal data heterogeneity in federated continual learning via tail anchor","author":"Yu","year":"2024","journal-title":"arXiv:2412.18355"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1117\/12.3032841"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-024-10314-z"},{"key":"ref134","doi-asserted-by":"publisher","DOI":"10.1109\/ICME57554.2024.10687881"},{"key":"ref135","doi-asserted-by":"publisher","DOI":"10.1109\/ICWS62655.2024.00022"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.23919\/DATE58400.2024.10546889"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1109\/BigData62323.2024.10825386"},{"key":"ref138","article-title":"Closed-form merging of parameter-efficient modules for federated continual learning","author":"Salami","year":"2024","journal-title":"arXiv:2410.17961"},{"key":"ref139","article-title":"Reducing bias in federated class-incremental learning with hierarchical generative prototypes","author":"Salami","year":"2024","journal-title":"arXiv:2406.02447"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1109\/CAI59869.2024.00153"},{"key":"ref141","first-page":"1","article-title":"Online structured laplace approximations for overcoming catastrophic forgetting","volume-title":"Proc. 32nd Conf. Neural Inf. Process. Syst.","volume":"31","author":"Ritter"},{"key":"ref142","first-page":"4528","article-title":"Progress & compress: A scalable framework for continual learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Schwarz"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3544605"},{"key":"ref144","article-title":"Federated orthogonal training: Mitigating global catastrophic forgetting in continual federated learning","author":"Bakman","year":"2023","journal-title":"arXiv:2309.01289"},{"key":"ref145","article-title":"Gradient projection memory for continual learning","author":"Saha","year":"2021","journal-title":"arXiv:2103.09762"},{"key":"ref146","article-title":"Overcoming forgetting in federated learning on non-IID data","author":"Shoham","year":"2019","journal-title":"arXiv:1910.07796"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1016\/j.tics.2020.09.004"},{"key":"ref148","article-title":"Progressive neural networks","author":"Rusu","year":"2016","journal-title":"arXiv:1606.04671"},{"key":"ref149","first-page":"907","article-title":"Reinforced continual learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Xu"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3090260"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-019-8208-z"},{"key":"ref152","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00060"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01179"},{"key":"ref154","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"ref155","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2105.07581"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671948"},{"key":"ref157","first-page":"40725","article-title":"Federated continual learning via prompt-based dual knowledge transfer","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Piao"},{"key":"ref158","article-title":"Federated class-incremental learning with prototype guided transformer","author":"Guo","year":"2024","journal-title":"arXiv:2401.02094"},{"key":"ref159","article-title":"Concept matching: Clustering-based federated continual learning","author":"Jiang","year":"2025","journal-title":"arXiv:2502.07059"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3299573"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-022-01046-x"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-78389-0_6"},{"key":"ref163","first-page":"8244","article-title":"A systematic survey on federated semi-supervised learning","volume-title":"Proc. IJCAI","author":"Song"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00990"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3327373"},{"key":"ref166","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00033"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.1109\/PIMRC56721.2023.10293844"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00424"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00534"},{"key":"ref170","article-title":"Towards federated learning on timeevolving heterogeneous data","author":"Guo","year":"2021","journal-title":"arXiv:2112.13246"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00553"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW60793.2023.00362"},{"key":"ref173","first-page":"1","article-title":"A Swiss army knife for heterogeneous federated learning: Flexible coupling via trace norm","volume-title":"Proc. 38th Annu. Conf. Neural Inf. Process. Syst.","author":"Liao"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00992"},{"key":"ref175","article-title":"State farm distracted driver detection.","author":"Montoya","year":"2016"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2016.56"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-022-00894-4"},{"key":"ref178","article-title":"Tailoring attacks to federated continual learning models","author":"Trinh","year":"2023"},{"key":"ref179","article-title":"Towards a defense against federated backdoor attacks under continuous training","author":"Wang","year":"2022","journal-title":"arXiv:2205.11736"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.1109\/BigData55660.2022.10021082"},{"key":"ref181","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2022.3195179"},{"key":"ref182","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.8332"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.15803\/ijnc.14.2_123"},{"key":"ref184","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_11"},{"key":"ref185","doi-asserted-by":"publisher","DOI":"10.1109\/TMLCN.2023.3344074"},{"key":"ref186","article-title":"Federated continual learning with differentially private data sharing","volume-title":"Proc. Workshop Federated Learn., Recent Adv. New Challenges (Conjunct. NeurIPS)","author":"Zizzo"},{"key":"ref187","article-title":"GFCL: A GRU-based federated continual learning framework against data poisoning attacks in IoV","author":"Talpur","year":"2022","journal-title":"arXiv:2204.11010"},{"key":"ref188","article-title":"Targeted backdoor attacks on deep learning systems using data poisoning","author":"Chen","year":"2017","journal-title":"arXiv:1712.05526"},{"key":"ref189","doi-asserted-by":"publisher","DOI":"10.1007\/11787006_1"},{"key":"ref190","article-title":"Federated intrusion detection for IoT with heterogeneous cohort privacy","author":"Chathoth","year":"2021","journal-title":"arXiv:2101.09878"},{"key":"ref191","doi-asserted-by":"publisher","DOI":"10.1109\/CANDAR64496.2024.00023"},{"key":"ref192","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11223814"},{"key":"ref193","doi-asserted-by":"publisher","DOI":"10.1145\/1007568.1007632"},{"key":"ref194","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"ref195","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN64981.2025.11228565"},{"key":"ref196","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73128-0_14"},{"key":"ref197","first-page":"1","article-title":"A classification of SQL injection attacks and countermeasures","volume-title":"Proc. ISSSE","author":"Halfond"},{"key":"ref198","article-title":"Federated continual learning to detect accounting anomalies in financial auditing","author":"Schreyer","year":"2022","journal-title":"arXiv:2210.15051"},{"key":"ref199","doi-asserted-by":"publisher","DOI":"10.1177\/0001839217751692"},{"key":"ref200","doi-asserted-by":"publisher","DOI":"10.1145\/3399742"},{"key":"ref201","article-title":"Federated continual learning for edge-AI: A comprehensive survey","author":"Wang","year":"2024","journal-title":"arXiv:2411.13740"},{"key":"ref202","doi-asserted-by":"publisher","DOI":"10.1109\/PerComWorkshops48775.2020.9156127"},{"key":"ref203","doi-asserted-by":"publisher","DOI":"10.1109\/RO-MAN57019.2023.10309661"},{"key":"ref204","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2025.3544614"},{"key":"ref205","doi-asserted-by":"publisher","DOI":"10.1109\/BigData62323.2024.10826139"},{"key":"ref206","doi-asserted-by":"publisher","DOI":"10.3390\/s18041212"},{"key":"ref207","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-023-05597-2"},{"key":"ref208","doi-asserted-by":"publisher","DOI":"10.1016\/j.vehcom.2021.100396"},{"key":"ref209","article-title":"FedCL-ensemble learning: A framework of federated continual learning with ensemble transfer learning enhanced for alzheimer\u2019s MRI classifications while preserving privacy","author":"Kapoor","year":"2024","journal-title":"arXiv:2411.12756"},{"key":"ref210","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/5164970"},{"key":"ref211","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2022.3185395"},{"key":"ref212","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2025.3535628"},{"key":"ref213","doi-asserted-by":"publisher","DOI":"10.23919\/JCIN.2024.10820161"},{"key":"ref214","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3282648"},{"issue":"4","key":"ref215","first-page":"1235","article-title":"Data sharing method of Industrial Internet of Things based on federal incremental learning","volume":"42","author":"Liu","year":"2022","journal-title":"J. Comput. Appl."},{"key":"ref216","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2019.2931179"},{"key":"ref217","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2022.10.123"},{"key":"ref218","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.09.019"},{"key":"ref219","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.018.2200349"},{"key":"ref220","doi-asserted-by":"publisher","DOI":"10.1109\/GEM61861.2024.10585565"},{"key":"ref221","article-title":"Continual deep reinforcement learning for decentralized satellite routing","author":"Lozano-Cuadra","year":"2024","journal-title":"arXiv:2405.12308"},{"key":"ref222","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3103016"},{"key":"ref223","doi-asserted-by":"publisher","DOI":"10.1109\/ICWS60048.2023.00068"},{"key":"ref224","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2023.121919"},{"key":"ref225","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3317930"},{"key":"ref226","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3295499"},{"key":"ref227","doi-asserted-by":"publisher","DOI":"10.3390\/math11081867"},{"key":"ref228","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11223668"},{"key":"ref229","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2990528"},{"key":"ref230","doi-asserted-by":"publisher","DOI":"10.1109\/TGCN.2022.3186898"},{"key":"ref231","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2022.01.006"},{"key":"ref232","doi-asserted-by":"publisher","DOI":"10.1145\/1064212.1064220"},{"key":"ref233","doi-asserted-by":"publisher","DOI":"10.1109\/ISI.2017.8004872"},{"key":"ref234","doi-asserted-by":"publisher","DOI":"10.1109\/MASS56207.2022.00055"},{"key":"ref235","article-title":"Asynchronous decentralized federated lifelong learning for landmark localization in medical imaging","author":"Zheng","year":"2023","journal-title":"arXiv:2303.06783"},{"key":"ref236","article-title":"A peer-topeer federated continual learning network for improving CT imaging from multiple institutions","author":"Wang","year":"2023","journal-title":"arXiv:2306.02037"},{"key":"ref237","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-024-03692-w"},{"key":"ref238","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-024-02464-9"},{"key":"ref239","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.02.003"},{"key":"ref240","article-title":"Continual federated learning for network anomaly detection in 5G open-RAN","author":"Hossain","year":"2023"},{"key":"ref241","doi-asserted-by":"publisher","DOI":"10.1109\/WCNC57260.2024.10570951"},{"key":"ref242","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3510553"},{"key":"ref243","article-title":"Continual learning for peer-to-peer federated learning: A study on automated brain metastasis identification","author":"Huang","year":"2022","journal-title":"arXiv:2204.13591"},{"key":"ref244","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.12.032"},{"key":"ref245","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13141"},{"key":"ref246","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-024-04697-9"},{"key":"ref247","article-title":"Rehearsalfree continual federated learning with synergistic regularization","author":"Li","year":"2024","journal-title":"arXiv:2412.13779"},{"key":"ref248","doi-asserted-by":"publisher","DOI":"10.1109\/LES.2024.3439552"},{"key":"ref249","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.11.038"},{"key":"ref250","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2025.121992"},{"key":"ref251","doi-asserted-by":"publisher","DOI":"10.3390\/s20226441"},{"key":"ref252","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2817685"},{"key":"ref253","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3367329"},{"key":"ref254","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00722"},{"key":"ref255","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"issue":"7","key":"ref256","first-page":"3","article-title":"Tiny ImageNet visual recognition challenge","volume":"7","author":"Le","year":"2015","journal-title":"CS 231N"},{"key":"ref257","volume-title":"MNIST Handwritten Digit Database","author":"LeCun","year":"2010"},{"key":"ref258","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref259","doi-asserted-by":"publisher","DOI":"10.1109\/34.291440"},{"key":"ref260","doi-asserted-by":"publisher","DOI":"10.2118\/18761-MS"},{"key":"ref261","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref262","doi-asserted-by":"publisher","DOI":"10.1109\/WACVW50321.2020.9096945"},{"key":"ref263","volume-title":"Estimating a Dirichlet Distribution","author":"Minka","year":"2000"},{"key":"ref264","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref265","article-title":"Resource-constrained federated continual learning: What does matter?","author":"Li","year":"2025","journal-title":"arXiv:2501.08737"},{"key":"ref266","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01548"},{"key":"ref267","article-title":"Learn from downstream and be yourself in multimodal large language model fine-tuning","author":"Huang","year":"2024","journal-title":"arXiv:2411.10928"},{"key":"ref268","doi-asserted-by":"publisher","DOI":"10.1145\/3705725"},{"key":"ref269","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.625"},{"key":"ref270","doi-asserted-by":"publisher","DOI":"10.1145\/3453154"},{"key":"ref271","article-title":"A complete survey on LLM-based ai chatbots","author":"Dam","year":"2024","journal-title":"arXiv:2406.16937"},{"key":"ref272","article-title":"Text-to-image diffusion models in generative AI: A survey","author":"Zhang","year":"2023","journal-title":"arXiv:2303.07909"}],"container-title":["IEEE Communications Surveys &amp; Tutorials"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/9739\/11321210\/10993375.pdf?arnumber=10993375","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T18:18:32Z","timestamp":1767377912000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10993375\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":272,"URL":"https:\/\/doi.org\/10.1109\/comst.2025.3568279","relation":{},"ISSN":["1553-877X","2373-745X"],"issn-type":[{"value":"1553-877X","type":"electronic"},{"value":"2373-745X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}