{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T22:01:20Z","timestamp":1782856880237,"version":"3.54.5"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004763","name":"Inner Mongolia Autonomous Region Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004763","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.eswa.2026.133283","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T06:36:16Z","timestamp":1781678176000},"page":"133283","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PC","title":["EA: Elastic multi-level anchoring-based generative approach for federated class-incremental learning"],"prefix":"10.1016","volume":"331","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1608-3933","authenticated-orcid":false,"given":"Wenxu","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2587-5180","authenticated-orcid":false,"given":"Lixin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tangpeng","family":"Dan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfeng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.133283_bib0001","doi-asserted-by":"crossref","first-page":"66408","DOI":"10.52202\/075280-2899","article-title":"A data-free approach to mitigate catastrophic forgetting in federated class incremental learning for vision tasks","volume":"36","author":"Babakniya","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.133283_bib0002","doi-asserted-by":"crossref","first-page":"66408","DOI":"10.52202\/075280-2899","article-title":"A data-free approach to mitigate catastrophic forgetting in federated class incremental learning for vision tasks","volume":"36","author":"Babakniya","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.133283_bib0003","article-title":"Federated continual learning for task-incremental and class-incremental problems: A survey","author":"Birashk","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.133283_bib0004","first-page":"1567","article-title":"Learning imbalanced datasets with label-distribution-aware margin loss","volume":"32","author":"Cao","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.133283_bib0005","series-title":"Proceedings of the european conference on computer vision (ECCV)","first-page":"532","article-title":"Riemannian walk for incremental learning: Understanding forgetting and intransigence","author":"Chaudhry","year":"2018"},{"issue":"3","key":"10.1016\/j.eswa.2026.133283_bib0006","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 Transactions on Network Science and Engineering"},{"issue":"1","key":"10.1016\/j.eswa.2026.133283_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2023.103532","article-title":"A reliable adaptive prototype-based learning for evolving data streams with limited labels","volume":"61","author":"Din","year":"2024","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.eswa.2026.133283_bib0008","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"10164","article-title":"Federated class-incremental learning","author":"Dong","year":"2022"},{"key":"10.1016\/j.eswa.2026.133283_bib0009","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"6597","article-title":"Up to 100x faster data-free knowledge distillation","volume":"vol. 36","author":"Fang","year":"2022"},{"key":"10.1016\/j.eswa.2026.133283_bib0010","series-title":"European conference on computer vision","first-page":"423","article-title":"R-DFCIL: Relation-guided representation learning for data-free class incremental learning","author":"Gao","year":"2022"},{"key":"10.1016\/j.eswa.2026.133283_bib0011","series-title":"2023\u202fIEEE International conference on multimedia and expo (ICME)","first-page":"432","article-title":"Privacy-enhanced zero-shot learning via data-free knowledge transfer","author":"Gao","year":"2023"},{"key":"10.1016\/j.eswa.2026.133283_bib0012","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"4205","article-title":"FedProK: Trustworthy federated class-incremental learning via prototypical feature knowledge transfer","author":"Gao","year":"2024"},{"key":"10.1016\/j.eswa.2026.133283_bib0013","unstructured":"Guo, H., Zeng, F., Zhu, F., Wang, J., Wang, X., Zhou, J., Zhao, H., Liu, W., Ma, S., Zhang, X.-Y. et al. (2025). A comprehensive survey on continual learning in generative models. arXiv preprint arXiv: 2506.13045."},{"key":"10.1016\/j.eswa.2026.133283_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110542","article-title":"Dynamic heterogeneous federated learning with multi-level prototypes","volume":"153","author":"Guo","year":"2024","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.eswa.2026.133283_bib0015","doi-asserted-by":"crossref","unstructured":"Hamedi, P., Razavi-Far, R., & Hallaji, E. (2025). Federated continual learning: Concepts, challenges, and solutions. arXiv preprint arXiv: 2502.07059.","DOI":"10.1016\/j.neucom.2025.130844"},{"issue":"1","key":"10.1016\/j.eswa.2026.133283_bib0016","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-97565-4","article-title":"Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings","volume":"15","author":"Haripriya","year":"2025","journal-title":"Scientific Reports"},{"key":"10.1016\/j.eswa.2026.133283_bib0017","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"2","key":"10.1016\/j.eswa.2026.133283_bib0018","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 Transactions on Consumer Electronics"},{"key":"10.1016\/j.eswa.2026.133283_bib0019","series-title":"Proc. CVPR workshop on fine-grained visual categorization (FGVC)","article-title":"Novel dataset for fine-grained image categorization: Stanford dogs","volume":"vol. 2","author":"Khosla","year":"2011"},{"key":"10.1016\/j.eswa.2026.133283_bib0020","series-title":"Proceedings of the national academy of sciences","first-page":"3521","article-title":"Overcoming catastrophic forgetting in neural networks","volume":"114","author":"Kirkpatrick","year":"2017"},{"key":"10.1016\/j.eswa.2026.133283_bib0021","series-title":"Technical report","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"10.1016\/j.eswa.2026.133283_bib0022","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.133283_bib0023","series-title":"Tiny ImageNet visual recognition challenge","first-page":"3","volume":"7","author":"Le","year":"2015"},{"key":"10.1016\/j.eswa.2026.133283_bib0024","doi-asserted-by":"crossref","first-page":"38461","DOI":"10.52202\/068431-2787","article-title":"Preservation of the global knowledge by not-true distillation in federated learning","volume":"35","author":"Lee","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.133283_bib0025","series-title":"2019 International joint conference on neural networks (IJCNN)","first-page":"1","article-title":"Generative models from the perspective of continual learning","author":"Lesort","year":"2019"},{"key":"10.1016\/j.eswa.2026.133283_bib0026","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li","year":"2020","journal-title":"Proceedings of Machine Learning and Systems"},{"issue":"12","key":"10.1016\/j.eswa.2026.133283_bib0027","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","article-title":"Learning without forgetting","volume":"40","author":"Li","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"5","key":"10.1016\/j.eswa.2026.133283_bib0028","doi-asserted-by":"crossref","first-page":"2868","DOI":"10.1007\/s11263-024-02303-4","article-title":"Relation-guided adversarial learning for data-free knowledge transfer","volume":"133","author":"Liang","year":"2025","journal-title":"International Journal of Computer Vision"},{"key":"10.1016\/j.eswa.2026.133283_bib0029","article-title":"Federated class-incremental learning with prompting","author":"Luo","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.133283_bib0030","unstructured":"Nguyen, T., Doan, K. D., Nguyen, B. T., Le-Phuoc, D., & Wong, K.-S. (2024). Overcoming catastrophic forgetting in federated class-incremental learning via federated global twin generator. arXiv preprint arXiv: 2407.11078."},{"key":"10.1016\/j.eswa.2026.133283_bib0031","series-title":"Proceedings of the AAAI conference on artificial intelligence","article-title":"Film: Visual reasoning with a general conditioning layer","volume":"vol. 32","author":"Perez","year":"2018"},{"key":"10.1016\/j.eswa.2026.133283_bib0032","unstructured":"Qi, Z., Tang, Y.-P., Meng, L., Yu, H., Li, X., & Meng, X. (2025). Class-wise balancing data replay for federated class-incremental learning. arXiv preprint arXiv: 2507.07712."},{"key":"10.1016\/j.eswa.2026.133283_bib0033","unstructured":"Salami, R., Buzzega, P., Mosconi, M., Bonato, J., Sabetta, L., & Calderara, S. (2024). Closed-form merging of parameter-efficient modules for federated continual learning. arXiv preprint arXiv: 2410.17961."},{"issue":"1","key":"10.1016\/j.eswa.2026.133283_bib0034","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/23744235.2024.2425712","article-title":"Role of artificial intelligence in early diagnosis and treatment of infectious diseases","volume":"57","author":"Srivastava","year":"2025","journal-title":"Infectious Diseases"},{"key":"10.1016\/j.eswa.2026.133283_bib0035","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"23870","article-title":"Text-enhanced data-free approach for federated class-incremental learning","author":"Tran","year":"2024"},{"key":"10.1016\/j.eswa.2026.133283_bib0036","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"23860","article-title":"Nayer: Noisy layer data generation for efficient and effective data-free knowledge distillation","author":"Tran","year":"2024"},{"key":"10.1016\/j.eswa.2026.133283_bib0037","doi-asserted-by":"crossref","first-page":"73775","DOI":"10.52202\/075280-3226","article-title":"TriRE: A multi-mechanism learning paradigm for continual knowledge retention and promotion","volume":"36","author":"Vijayan","year":"2023","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.133283_bib0038","unstructured":"Wang, L., Xu, S., Xu, R., Wang, X., & Zhu, Q. (2021). Non-transferable learning: A new approach for model ownership verification and applicability authorization. arXiv preprint arXiv: 2106.06916."},{"key":"10.1016\/j.eswa.2026.133283_bib0039","unstructured":"Wang, N., Deng, Y., Feng, W., Yin, J., & Ng, S.-K. (2024). Data-free federated class incremental learning with diffusion-based generative memory. arXiv preprint arXiv: 2405.17457."},{"key":"10.1016\/j.eswa.2026.133283_bib0040","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":"Information Sciences"},{"issue":"3","key":"10.1016\/j.eswa.2026.133283_bib0041","doi-asserted-by":"crossref","first-page":"3821","DOI":"10.1109\/TNNLS.2022.3199816","article-title":"Incremental embedding learning with disentangled representation translation","volume":"35","author":"Wei","year":"2022","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"6","key":"10.1016\/j.eswa.2026.133283_bib0042","doi-asserted-by":"crossref","first-page":"2526","DOI":"10.1109\/TAI.2023.3339091","article-title":"Continual learning: A review of techniques, challenges, and future directions","volume":"5","author":"Wickramasinghe","year":"2023","journal-title":"IEEE Transactions on Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.133283_bib0043","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.113067","article-title":"Strategic integration of adaptive sampling and ensemble techniques in federated learning for aircraft engine remaining useful life prediction","volume":"175","author":"Xu","year":"2025","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.eswa.2026.133283_bib0044","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"8715","article-title":"Dreaming to distill: Data-free knowledge transfer via deepinversion","author":"Yin","year":"2020"},{"key":"10.1016\/j.eswa.2026.133283_bib0045","series-title":"Proceedings of the asian conference on computer vision","first-page":"488","article-title":"Federated class incremental learning: A pseudo feature based approach without exemplars","author":"Yoo","year":"2024"},{"key":"10.1016\/j.eswa.2026.133283_bib0046","series-title":"International conference on machine learning","first-page":"12073","article-title":"Federated continual learning with weighted inter-client transfer","author":"Yoon","year":"2021"},{"key":"10.1016\/j.eswa.2026.133283_bib0047","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.128442","article-title":"Adaptive federated class-incremental learning for reducing catastrophic forgetting","author":"You","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.133283_bib0048","series-title":"Proceedings of the computer vision and pattern recognition conference","first-page":"4874","article-title":"Handling spatial-temporal data heterogeneity for federated continual learning via tail anchor","author":"Yu","year":"2025"},{"key":"10.1016\/j.eswa.2026.133283_bib0049","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"4782","article-title":"Target: Federated class-continual learning via exemplar-free distillation","author":"Zhang","year":"2023"},{"key":"10.1016\/j.eswa.2026.133283_bib0050","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"16768","article-title":"FedTGP: Trainable global prototypes with adaptive-margin-enhanced contrastive learning for data and model heterogeneity in federated learning","volume":"vol. 38","author":"Zhang","year":"2024"},{"key":"10.1016\/j.eswa.2026.133283_bib0051","series-title":"Proceedings of the computer vision and pattern recognition conference","first-page":"30640","article-title":"pFedMxF: Personalized federated class-incremental learning with mixture of frequency aggregation","author":"Zhang","year":"2025"},{"key":"10.1016\/j.eswa.2026.133283_bib0052","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"23009","article-title":"FedSA: A unified representation learning via semantic anchors for prototype-based federated learning","volume":"vol. 39","author":"Zhou","year":"2025"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426021925?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426021925?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T20:58:30Z","timestamp":1782853110000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426021925"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":52,"alternative-id":["S0957417426021925"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.133283","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"EA: Elastic multi-level anchoring-based generative approach for federated class-incremental learning","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.133283","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"133283"}}