{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T12:13:17Z","timestamp":1783944797025,"version":"3.55.0"},"reference-count":98,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100021856","name":"Ministero dell'Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100021856","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100024370","name":"Ministero dell'Istruzione dell'Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100024370","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.neucom.2026.133929","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T11:08:29Z","timestamp":1778756909000},"page":"133929","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data"],"prefix":"10.1016","volume":"694","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5221-8197","authenticated-orcid":false,"given":"Masoume","family":"Gholizade","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6328-4360","authenticated-orcid":false,"given":"Fabrizio","family":"Ruffini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4510-1350","authenticated-orcid":false,"given":"Pietro","family":"Ducange","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5895-876X","authenticated-orcid":false,"given":"Francesco","family":"Marcelloni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.neucom.2026.133929_bib0005","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1109\/TNSE.2023.3292805","article-title":"FedStream: a federated learning framework on heterogeneous streaming data for next-generation traffic analysis","volume":"11","author":"Wang","year":"2024","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"10.1016\/j.neucom.2026.133929_bib0010","series-title":"Proceedings of the 3rd Italian Workshop on Explainable Artificial Intelligence (XAI.it 2022), Vol. 3277 of CEUR Workshop Proceedings","article-title":"Fed-XAI: federated learning of explainable artificial intelligence models","author":"Corcuera B\u00e1rcena","year":"2022"},{"key":"10.1016\/j.neucom.2026.133929_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2025.111479","article-title":"Federated learning of explainable AI(FedXAI) for deep learning-based intrusion detection in IoT networks","volume":"270","author":"Kalakoti","year":"2025","journal-title":"Comput. Netw."},{"key":"10.1016\/j.neucom.2026.133929_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106492","article-title":"Continual pre-training mitigates forgetting in language and vision","volume":"179","author":"Cossu","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.neucom.2026.133929_bib0025","article-title":"Continual learning with knowledge distillation: a survey","author":"Li","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.neucom.2026.133929_bib0030","article-title":"FedMTL: adaptive multi-teacher knowledge distillation for federated continual learning","author":"Chen","year":"2025","journal-title":"Knowl.-based Syst."},{"key":"10.1016\/j.neucom.2026.133929_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108826","article-title":"Federated continual representation learning for evolutionary distributed intrusion detection in industrial internet of things","volume":"135","author":"Zhang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"8","key":"10.1016\/j.neucom.2026.133929_bib0040","doi-asserted-by":"crossref","first-page":"5362","DOI":"10.1109\/TPAMI.2024.3367329","article-title":"A comprehensive survey of continual learning: theory, method and application","volume":"46","author":"Wang","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2026.133929_bib0045","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.inffus.2022.07.024","article-title":"Non-iid data and continual learning processes in federated learning: a long road ahead","volume":"88","author":"Criado","year":"2022","journal-title":"Inf. Fusion"},{"issue":"8","key":"10.1016\/j.neucom.2026.133929_bib0050","doi-asserted-by":"crossref","first-page":"3832","DOI":"10.1109\/TKDE.2024.3363240","article-title":"Federated continual learning via knowledge fusion: a survey","volume":"36","author":"Yang","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2026.133929_bib0055","author":"Wang"},{"key":"10.1016\/j.neucom.2026.133929_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130844","article-title":"Federated continual learning: concepts, challenges, and solutions","volume":"651","author":"Hamedi","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133929_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.129278","article-title":"Federated continual learning for task-incremental and class-incremental problems: a survey","volume":"297","author":"Birashk","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133929_bib0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2026.133366","article-title":"Federated continual learning meets digital twins: a survey on methods, intersections and perspectives","volume":"681","author":"Savoia","year":"2026","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133929_bib0075","article-title":"Heterogeneous federated learning: state-of-the-art and research challenges","author":"Ye","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.neucom.2026.133929_bib0080","doi-asserted-by":"crossref","first-page":"2195","DOI":"10.1007\/s00607-023-01179-5","article-title":"A docker-based federated learning framework design and deployment for multi-modal data stream classification","volume":"105","author":"Nandi","year":"2023","journal-title":"Computing"},{"key":"10.1016\/j.neucom.2026.133929_bib0085","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.ymeth.2022.03.005","article-title":"A federated learning method for real-time emotion state classification from multi-modal streaming","volume":"204","author":"Nandi","year":"2022","journal-title":"Methods"},{"issue":"5","key":"10.1016\/j.neucom.2026.133929_bib0090","doi-asserted-by":"crossref","first-page":"4879","DOI":"10.1109\/TDSC.2024.3364060","article-title":"A federated learning framework based on differentially private continuous data release","volume":"21","author":"Cai","year":"2024","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"10.1016\/j.neucom.2026.133929_bib0095","series-title":"2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","article-title":"An approach to federated learning of explainable fuzzy regression models","author":"Corcuera B\u00e1rcena","year":"2022"},{"key":"10.1016\/j.neucom.2026.133929_bib0100","doi-asserted-by":"crossref","DOI":"10.1109\/TCSS.2025.3606798","article-title":"An explainable and privacy-preserving federated learning model for threat detection in cyber-physical-social systems","author":"Yazdinejad","year":"2025","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"10.1016\/j.neucom.2026.133929_bib0105","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.aej.2024.07.040","article-title":"An effective federated learning system for industrial IoT data streaming","volume":"105","author":"Wu","year":"2024","journal-title":"Alex. Eng. J."},{"issue":"12","key":"10.1016\/j.neucom.2026.133929_bib0110","doi-asserted-by":"crossref","first-page":"3704","DOI":"10.1109\/JSAC.2021.3118421","article-title":"Budget-aware online control of edge federated learning on streaming data with stochastic inputs","volume":"39","author":"Jin","year":"2021","journal-title":"IEEE J. Sel. Areas Commun."},{"issue":"12","key":"10.1016\/j.neucom.2026.133929_bib0115","article-title":"Communication-efficient federated continual learning for distributed learning system with non-IID data","volume":"66","author":"Zhang","year":"2023","journal-title":"Sci. China Inf. Sci."},{"issue":"24449","key":"10.1016\/j.neucom.2026.133929_bib0120","article-title":"Cross paradigm fusion of federated and continual learning on multilayer perceptron mixer architecture for incremental thoracic infection diagnosis","volume":"15","author":"Zhou","year":"2025","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.neucom.2026.133929_bib0125","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1109\/TMC.2022.3223944","article-title":"Cross-FCL: toward a cross-edge federated continual learning framework in mobile edge computing systems","volume":"23","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Mob. Comput."},{"key":"10.1016\/j.neucom.2026.133929_bib0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.sysarc.2025.103582","article-title":"A survey of optimization algorithms for differential privacy in federated learning","author":"Shan","year":"2025","journal-title":"J. Syst. Archit."},{"issue":"7","key":"10.1016\/j.neucom.2026.133929_bib0135","doi-asserted-by":"crossref","first-page":"7664","DOI":"10.1109\/TMC.2023.3337016","article-title":"DYNAMITE: dynamic interplay of mini-batch size and aggregation frequency for federated learning with static and streaming datasets","volume":"23","author":"Liu","year":"2024","journal-title":"IEEE Trans. Mob. Comput."},{"key":"10.1016\/j.neucom.2026.133929_bib0140","series-title":"2021 IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"8230","article-title":"Continual prototype evolution: learning online from non-stationary data streams","author":"De Lange","year":"2021"},{"issue":"5","key":"10.1016\/j.neucom.2026.133929_bib0145","doi-asserted-by":"crossref","DOI":"10.1145\/3735633","article-title":"Continual learning of large language models: a comprehensive survey","volume":"58","author":"Shi","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.neucom.2026.133929_bib0150","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2001","article-title":"ICARL: incremental classifier and representation learning","author":"Rebuffi","year":"2017"},{"key":"10.1016\/j.neucom.2026.133929_bib0155","series-title":"Advances in Neural Information Processing Systems, 32","article-title":"Experience replay for continual learning","author":"Rolnick","year":"2019"},{"issue":"12","key":"10.1016\/j.neucom.2026.133929_bib0160","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","article-title":"Learning without forgetting","volume":"40","author":"Li","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"10.1016\/j.neucom.2026.133929_bib0165","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1038\/s41551-022-00914-1","article-title":"Self-supervised learning in medicine and healthcare","volume":"6","author":"Krishnan","year":"2022","journal-title":"Nat. Biomed. Eng."},{"issue":"5","key":"10.1016\/j.neucom.2026.133929_bib0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104157","article-title":"Continual and wisdom learning for federated learning: a comprehensive framework for robustness and debiasing","volume":"62","author":"Iqbal","year":"2025","journal-title":"Inf. Process. Manag."},{"key":"10.1016\/j.neucom.2026.133929_bib0175","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.future.2024.02.018","article-title":"Efficient knowledge management for heterogeneous federated continual learning on resource-constrained edge devices","volume":"156","author":"Yang","year":"2024","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.neucom.2026.133929_bib0180","doi-asserted-by":"crossref","first-page":"24462","DOI":"10.1109\/ACCESS.2021.3056919","article-title":"Client selection for federated learning with non-IID data in mobile edge computing","volume":"9","author":"Zhang","year":"2021","journal-title":"IEEE Access"},{"issue":"7","key":"10.1016\/j.neucom.2026.133929_bib0185","doi-asserted-by":"crossref","first-page":"2803","DOI":"10.1007\/s12559-024-10314-z","article-title":"Towards long-term remembering in federated continual learning","volume":"16","author":"Zhao","year":"2024","journal-title":"Cogn. Comput."},{"issue":"22","key":"10.1016\/j.neucom.2026.133929_bib0190","doi-asserted-by":"crossref","first-page":"45958","DOI":"10.1109\/JIOT.2025.3535628","article-title":"Federated continual learning based on weakly supervised diffusion models for disease diagnosis","volume":"12","author":"Sun","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.neucom.2026.133929_bib0195","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.jclinepi.2021.02.003","article-title":"Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement","volume":"134","author":"Page","year":"2021","journal-title":"J. Clin. Epidemiol."},{"issue":"8","key":"10.1016\/j.neucom.2026.133929_bib0200","doi-asserted-by":"crossref","first-page":"3874","DOI":"10.1109\/TCYB.2021.3090260","article-title":"Federated continuous learning with broad network architecture","volume":"51","author":"Le","year":"2021","journal-title":"IEEE Trans. Cybern."},{"issue":"9","key":"10.1016\/j.neucom.2026.133929_bib0205","doi-asserted-by":"crossref","first-page":"3413","DOI":"10.1007\/s10994-023-06330-z","article-title":"Ensemble and continual federated learning for classification tasks","volume":"112","author":"Casado","year":"2023","journal-title":"Mach. Learn."},{"issue":"11","key":"10.1016\/j.neucom.2026.133929_bib0210","doi-asserted-by":"crossref","first-page":"7112","DOI":"10.1109\/TSMC.2023.3293462","article-title":"FedStream: prototype-based federated learning on distributed concept-drifting data streams","volume":"53","author":"Mawuli","year":"2023","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"10.1016\/j.neucom.2026.133929_bib0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2023.119235","article-title":"Semi-supervised federated learning on evolving data streams","volume":"643","author":"Mawuli","year":"2023","journal-title":"Inf. Sci."},{"issue":"22","key":"10.1016\/j.neucom.2026.133929_bib0220","doi-asserted-by":"crossref","first-page":"37187","DOI":"10.1109\/JIOT.2024.3440029","article-title":"PI-fed: continual federated learning with parameter-level importance aggregation","volume":"11","author":"Yu","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.neucom.2026.133929_bib0225","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.ins.2023.02.015","article-title":"Federated probability memory recall for federated continual learning","volume":"629","author":"Wang","year":"2023","journal-title":"Inf. Sci."},{"issue":"6","key":"10.1016\/j.neucom.2026.133929_bib0230","doi-asserted-by":"crossref","first-page":"10808","DOI":"10.1109\/JIOT.2023.3327316","article-title":"On the local cache update rules in streaming federated learning","volume":"11","author":"Wang","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.neucom.2026.133929_bib0235","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.comcom.2022.09.016","article-title":"AFAFed\u2014asynchronous fair adaptive federated learning for IoT stream applications","volume":"195","author":"Baccarelli","year":"2022","journal-title":"Comput. Commun."},{"issue":"3","key":"10.1016\/j.neucom.2026.133929_bib0240","doi-asserted-by":"crossref","first-page":"3055","DOI":"10.1109\/TCC.2023.3254587","article-title":"HFedMS: heterogeneous federated learning with memorable data semantics in industrial metaverse","volume":"11","author":"Zeng","year":"2023","journal-title":"IEEE Trans. Cloud Comput."},{"key":"10.1016\/j.neucom.2026.133929_bib0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.129882","article-title":"AdaptiveStreamFL: a Bayesian-enhanced multi-scale federated learning framework for dynamic data streams with uncertainty quantification","volume":"299","author":"Xiong","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133929_bib0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111491","article-title":"Facing spatiotemporal heterogeneity: a unified federated continual learning framework with self-challenge rehearsal for industrial monitoring tasks","volume":"289","author":"Li","year":"2024","journal-title":"Knowl.-based Syst."},{"issue":"20","key":"10.1016\/j.neucom.2026.133929_bib0255","doi-asserted-by":"crossref","first-page":"42103","DOI":"10.1109\/JIOT.2025.3592954","article-title":"Federated learning on multilabel evolving data streams","volume":"12","author":"Lamptey","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.neucom.2026.133929_bib0260","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2026.133365","article-title":"FedCapD: federated class-incremental learning via capsule distillation and diffusion replay","author":"Iqbal","year":"2026","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133929_bib0265","article-title":"FedMTL: adaptive multi-teacher knowledge distillation for federated continual learning","author":"Chen","year":"2026","journal-title":"Knowl.-based Syst."},{"issue":"4","key":"10.1016\/j.neucom.2026.133929_bib0270","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1109\/TSC.2025.3583174","article-title":"Sparse-FCL: sparse federated continual learning for evolving mobile edge computing environments","volume":"18","author":"Liu","year":"2025","journal-title":"IEEE Trans. Serv. Comput."},{"issue":"3","key":"10.1016\/j.neucom.2026.133929_bib0275","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1109\/TNSE.2025.3544614","article-title":"Knowledge efficient federated continual learning for industrial edge systems","volume":"12","author":"Chen","year":"2025","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"23","key":"10.1016\/j.neucom.2026.133929_bib0280","doi-asserted-by":"crossref","first-page":"20776","DOI":"10.1109\/JIOT.2023.3284843","article-title":"Personalized federated continual learning for task-incremental biometrics","volume":"10","author":"Li","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.neucom.2026.133929_bib0285","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.ins.2023.02.003","article-title":"A federated learning and blockchain framework for physiological signal classification based on continual learning","volume":"630","author":"Sun","year":"2023","journal-title":"Inf. Sci."},{"key":"10.1016\/j.neucom.2026.133929_bib0290","doi-asserted-by":"crossref","DOI":"10.1016\/j.iot.2023.101036","article-title":"Towards flexible data stream collaboration: federated learning in kafka-ML","volume":"25","author":"Chaves","year":"2024","journal-title":"Internet Things"},{"issue":"11","key":"10.1016\/j.neucom.2026.133929_bib0295","doi-asserted-by":"crossref","first-page":"6199","DOI":"10.1109\/TFUZZ.2024.3443207","article-title":"Promoting objective knowledge transfer: a cascaded fuzzy system for solving dynamic multiobjective optimization problems","volume":"32","author":"Li","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.neucom.2026.133929_bib0300","doi-asserted-by":"crossref","first-page":"10287","DOI":"10.1109\/TIFS.2024.3477325","article-title":"Boosting accuracy of differentially private continuous data release for federated learning","volume":"19","author":"Cai","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.neucom.2026.133929_bib0305","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128273","article-title":"FGS-FL: enhancing federated learning with differential privacy via flat gradient stream","volume":"288","author":"Hu","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133929_bib0310","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.127601","article-title":"A novel population robustness-based switching response framework for solving dynamic multi-objective problems","volume":"583","author":"Li","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2026.133929_bib0315","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2023.109556","article-title":"Online training data acquisition for federated learning in cloud-edge networks","author":"Zhu","year":"2023","journal-title":"Comput. Netw."},{"key":"10.1016\/j.neucom.2026.133929_bib0320","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2024.110517","article-title":"Communication cost-aware client selection in online federated learning: a Lyapunov approach","author":"Su","year":"2024","journal-title":"Comput. Netw."},{"issue":"15","key":"10.1016\/j.neucom.2026.133929_bib0325","doi-asserted-by":"crossref","DOI":"10.3390\/electronics14152945","article-title":"DPAO-PFL: dynamic parameter-aware optimization via continual learning for personalized federated learning","volume":"14","author":"Tang","year":"2025","journal-title":"Electronics"},{"issue":"1","key":"10.1016\/j.neucom.2026.133929_bib0330","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s13636-023-00299-2","article-title":"Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning","volume":"2023","author":"Chen","year":"2023","journal-title":"EURASIP J. Audio Speech Music Process."},{"key":"10.1016\/j.neucom.2026.133929_bib0335","first-page":"1","article-title":"Meta representation-based personalized federated continual learning in edge computing systems","author":"Zheng","year":"2025","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"issue":"9","key":"10.1016\/j.neucom.2026.133929_bib0340","doi-asserted-by":"crossref","first-page":"17169","DOI":"10.1109\/TNNLS.2025.3565827","article-title":"SacFL: self-adaptive federated continual learning for resource-constrained end devices","volume":"36","author":"Zhong","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.neucom.2026.133929_bib0345","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.131459","article-title":"Enhancing long-term memory in federated class continual learning with lightweight adapters","volume":"656","author":"Wang","year":"2025","journal-title":"Neurocomputing"},{"issue":"2","key":"10.1016\/j.neucom.2026.133929_bib0350","doi-asserted-by":"crossref","first-page":"5025","DOI":"10.1109\/TCE.2025.3563909","article-title":"Hierarchical continual learning for domain-knowledge retention in healthcare federated learning","volume":"71","author":"Iqbal","year":"2025","journal-title":"IEEE Trans. Consum. Electron."},{"issue":"4","key":"10.1016\/j.neucom.2026.133929_bib0355","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1109\/TPDS.2025.3531123","article-title":"Loci: federated continual learning of heterogeneous tasks at edge","volume":"36","author":"Luopan","year":"2025","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.neucom.2026.133929_bib0360","doi-asserted-by":"crossref","DOI":"10.1016\/j.wasman.2025.114976","article-title":"Federated continual learning for vision-based plastic classification in recycling","volume":"205","author":"Shami","year":"2025","journal-title":"Waste Manag."},{"key":"10.1016\/j.neucom.2026.133929_bib0365","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2025.111448","article-title":"Explainable federated class incremental learning for encrypted network traffic classification","author":"Carillo","year":"2025","journal-title":"Comput. Netw."},{"key":"10.1016\/j.neucom.2026.133929_bib0370","doi-asserted-by":"crossref","DOI":"10.1016\/j.future.2023.09.019","article-title":"FL-IIDS: a novel federated learning-based incremental intrusion detection system","author":"Jin","year":"2024","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.neucom.2026.133929_bib0375","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107920","article-title":"Family-based continual learning for multi-domain pattern analysis in federated frameworks with GCN and ViT","volume":"192","author":"Iqbal","year":"2025","journal-title":"Neural Netw."},{"key":"10.1016\/j.neucom.2026.133929_bib0380","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1007\/s11042-021-11219-x","article-title":"Concept drift detection and adaptation for federated and continual learning","volume":"81","author":"Casado","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.neucom.2026.133929_bib0385","article-title":"Federated learning based gender classification in heterogeneous and distributed data having concept drift","author":"Sharma","year":"2025","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.neucom.2026.133929_bib0390","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2025.104361","article-title":"M2fd: mobile malware federated detection under concept drift","author":"Augello","year":"2025","journal-title":"Comput. Secur."},{"key":"10.1016\/j.neucom.2026.133929_bib0395","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijepes.2025.110779","article-title":"Federated online learning for adaptive load forecasting across decentralized nodes","author":"Elgalhud","year":"2025","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"10.1016\/j.neucom.2026.133929_bib0400","doi-asserted-by":"crossref","DOI":"10.1016\/j.iot.2024.101340","article-title":"DISFIDA: distributed self-supervised federated intrusion detection algorithm with online learning for health IoT and IoV","author":"Gelenbe","year":"2024","journal-title":"Internet Things"},{"key":"10.1016\/j.neucom.2026.133929_bib0405","doi-asserted-by":"crossref","DOI":"10.1109\/TSP.2026.3673260","article-title":"Dynamic regret for byzantine-robust online federated learning","author":"Tian","year":"2026","journal-title":"IEEE Trans. Signal Process."},{"issue":"18","key":"10.1016\/j.neucom.2026.133929_bib0410","doi-asserted-by":"crossref","first-page":"37777","DOI":"10.1109\/JIOT.2025.3583978","article-title":"Online transient stability assessment under concept drift: an ARF-method-assisted federated learning for data streams","volume":"12","author":"Massaoudi","year":"2025","journal-title":"IEEE Internet Things J."},{"issue":"12","key":"10.1016\/j.neucom.2026.133929_bib0415","doi-asserted-by":"crossref","first-page":"4871","DOI":"10.1109\/TCAD.2023.3274956","article-title":"Self-supervised on-device federated learning from unlabeled streams","volume":"42","author":"Shi","year":"2023","journal-title":"IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst."},{"key":"10.1016\/j.neucom.2026.133929_bib0420","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuri.2026.100272","article-title":"A new communication efficient federated online distillation for non-IID data: application to fetal brain ultrasound plane classification","author":"Salman","year":"2026","journal-title":"Neurosci. Inform."},{"issue":"4","key":"10.1016\/j.neucom.2026.133929_bib0425","doi-asserted-by":"crossref","first-page":"362","DOI":"10.23919\/JCIN.2024.10820161","article-title":"FCLA-DT: federated continual learning with authentication for distributed digital twin-based industrial IoT","volume":"9","author":"Xia","year":"2024","journal-title":"J. Commun. Inf. Netw."},{"issue":"6","key":"10.1016\/j.neucom.2026.133929_bib0430","doi-asserted-by":"crossref","first-page":"6070","DOI":"10.1109\/JIOT.2024.3510553","article-title":"FCLLM-DT: enpowering federated continual learning with large language models for digital-twin-based industrial IoT","volume":"12","author":"Xia","year":"2025","journal-title":"IEEE Internet Things J."},{"issue":"11","key":"10.1016\/j.neucom.2026.133929_bib0435","doi-asserted-by":"crossref","first-page":"12565","DOI":"10.1109\/TII.2024.3423314","article-title":"FedCov: enhanced trustworthy federated learning for machine RUL prediction with continuous-to-discrete conversion","volume":"20","author":"Cai","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"4","key":"10.1016\/j.neucom.2026.133929_bib0440","doi-asserted-by":"crossref","DOI":"10.3390\/electronics12040894","article-title":"Intruder detection in VANET data streams using federated learning for smart city environments","volume":"12","author":"Arya","year":"2023","journal-title":"Electronics"},{"issue":"18","key":"10.1016\/j.neucom.2026.133929_bib0445","doi-asserted-by":"crossref","DOI":"10.3390\/app131810203","article-title":"Hybrid modeling for stream flow estimation: integrating machine learning and federated learning","volume":"13","author":"Akbulut","year":"2023","journal-title":"Appl. Sci."},{"key":"10.1016\/j.neucom.2026.133929_bib0450","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107345","article-title":"Self-attention fusion and adaptive continual updating for multimodal federated learning with heterogeneous data","volume":"187","author":"Yin","year":"2025","journal-title":"Neural Netw."},{"issue":"6","key":"10.1016\/j.neucom.2026.133929_bib0455","doi-asserted-by":"crossref","first-page":"6070","DOI":"10.1109\/JIOT.2024.3510553","article-title":"FCLLM-DT: enpowering federated continual learning with large language models for digital-twin-based industrial IoT","volume":"12","author":"Xia","year":"2025","journal-title":"IEEE Internet Things J."},{"issue":"2","key":"10.1016\/j.neucom.2026.133929_bib0460","doi-asserted-by":"crossref","first-page":"1884","DOI":"10.1109\/TCSVT.2023.3281983","article-title":"Spatial-temporal federated learning for lifelong person re-identification on distributed edges","volume":"35","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.neucom.2026.133929_bib0465","series-title":"2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"9611","article-title":"Self-supervised models are continual learners","author":"Fini","year":"2022"},{"key":"10.1016\/j.neucom.2026.133929_bib0470","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2025.104137","article-title":"Trustworthy and explainable federated system for extracting descriptive rules in a data streaming environment","author":"Padilla-Rasc\u00f3n","year":"2025","journal-title":"Results Eng."},{"key":"10.1016\/j.neucom.2026.133929_bib0475","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1007\/s10586-025-05609-1","article-title":"Differential privacy-enabled federated learning for secure neural synchronization in protecting industrial data streams","volume":"28","author":"Niu","year":"2025","journal-title":"Clust. Comput."},{"key":"10.1016\/j.neucom.2026.133929_bib0480","doi-asserted-by":"crossref","DOI":"10.1016\/j.automatica.2023.111460","article-title":"SHED: a Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing","volume":"160","author":"Dal Fabbro","year":"2024","journal-title":"Automatica"},{"key":"10.1016\/j.neucom.2026.133929_bib0485","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103473","article-title":"Choir-IDS: a federated learning framework for fidelity-calibrated explainable intrusion detection system for edge-IoT networks","author":"Sahoo","year":"2026","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.neucom.2026.133929_bib0490","doi-asserted-by":"crossref","DOI":"10.3389\/fviro.2025.1625855","article-title":"Overcoming diagnostic and data privacy challenges in viral disease detection: an integrated approach using generative AI, vision transformers, explainable AI, and federated learning","author":"Srinivasulu","year":"2025","journal-title":"Front. Virol."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226013263?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226013263?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T11:54:14Z","timestamp":1783943654000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226013263"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":98,"alternative-id":["S0925231226013263"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133929","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133929","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133929"}}