{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T08:06:47Z","timestamp":1780646807518,"version":"3.54.1"},"reference-count":55,"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"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Displays"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.displa.2026.103554","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T16:04:20Z","timestamp":1779984260000},"page":"103554","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["AFCL-CSC: Asynchronous Federated Continual Learning with Client\u2013Server Cooperative optimization"],"prefix":"10.1016","volume":"95","author":[{"given":"Dairao","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weidong","family":"Bao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengyi","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiongtao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengrong","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.displa.2026.103554_b1","series-title":"GLOBECOM 2024-2024 IEEE Global Communications Conference","first-page":"241","article-title":"Edge-cloud enabled smart sensing applications with personalized federated learning in IoT","author":"Mao","year":"2024"},{"issue":"3","key":"10.1016\/j.displa.2026.103554_b2","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","article-title":"Federated learning in mobile edge networks: A comprehensive survey","volume":"22","author":"Lim","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"10.1016\/j.displa.2026.103554_b3","doi-asserted-by":"crossref","first-page":"85714","DOI":"10.1109\/ACCESS.2020.2991734","article-title":"An overview on edge computing research","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"issue":"5","key":"10.1016\/j.displa.2026.103554_b4","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","article-title":"Edge computing: Vision and challenges","volume":"3","author":"Shi","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.displa.2026.103554_b5","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.future.2019.02.050","article-title":"Edge computing: A survey","volume":"97","author":"Khan","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.displa.2026.103554_b6","series-title":"Artificial Intelligence and Statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"key":"10.1016\/j.displa.2026.103554_b7","unstructured":"X.C. Li, Y.C. Xu, S. Song, B. Li, Y. Li, Y. Shao, D.C. Zhan, Federated learning with position-aware neurons, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10082\u201310091."},{"key":"10.1016\/j.displa.2026.103554_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2020.106854","article-title":"A review of applications in federated learning","volume":"149","author":"Li","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.displa.2026.103554_b9","series-title":"Federated learning: Opportunities and challenges","author":"Mammen","year":"2021"},{"key":"10.1016\/j.displa.2026.103554_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.106775","article-title":"A survey on federated learning","volume":"216","author":"Zhang","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.displa.2026.103554_b11","series-title":"2023 IEEE 12th Global Conference on Consumer Electronics","first-page":"1050","article-title":"Factors affecting user experience preferences in short video platforms-a case study of TikTok","author":"Feng","year":"2023"},{"key":"10.1016\/j.displa.2026.103554_b12","series-title":"2022 2nd International Conference on Big Data Engineering and Education","first-page":"53","article-title":"Research on the influencing factors of the user information cocoon effect of short video platforms based on personalized recommendation algorithms","author":"Wang","year":"2022"},{"issue":"4","key":"10.1016\/j.displa.2026.103554_b13","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/S1364-6613(99)01294-2","article-title":"Catastrophic forgetting in connectionist networks","volume":"3","author":"French","year":"1999","journal-title":"Trends Cogn. Sci."},{"key":"10.1016\/j.displa.2026.103554_b14","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"},{"key":"10.1016\/j.displa.2026.103554_b15","doi-asserted-by":"crossref","unstructured":"D. Shenaj, M. Toldo, A. Rigon, P. Zanuttigh, Asynchronous federated continual learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 5055\u20135063.","DOI":"10.1109\/CVPRW59228.2023.00534"},{"key":"10.1016\/j.displa.2026.103554_b16","series-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"issue":"6","key":"10.1016\/j.displa.2026.103554_b17","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1109\/TPAMI.2019.2963387","article-title":"Direction concentration learning: Enhancing congruency in machine learning","volume":"43","author":"Luo","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8","key":"10.1016\/j.displa.2026.103554_b18","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."},{"issue":"7","key":"10.1016\/j.displa.2026.103554_b19","first-page":"3366","article-title":"A continual learning survey: Defying forgetting in classification tasks","volume":"44","author":"De Lange","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"10.1016\/j.displa.2026.103554_b20","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1016\/j.tics.2020.09.004","article-title":"Embracing change: Continual learning in deep neural networks","volume":"24","author":"Hadsell","year":"2020","journal-title":"Trends Cogn. Sci."},{"key":"10.1016\/j.displa.2026.103554_b21","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.inffus.2019.12.004","article-title":"Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges","volume":"58","author":"Lesort","year":"2020","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.displa.2026.103554_b22","series-title":"Multi-task incremental learning for object detection","author":"Liu","year":"2020"},{"issue":"12","key":"10.1016\/j.displa.2026.103554_b23","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1038\/s42256-022-00568-3","article-title":"Three types of incremental learning","volume":"4","author":"Van de Ven","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.displa.2026.103554_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111882","article-title":"Domain incremental learning for object detection","volume":"170","author":"Luo","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.displa.2026.103554_b25","doi-asserted-by":"crossref","first-page":"15027","DOI":"10.52202\/075280-0660","article-title":"A unified approach to domain incremental learning with memory: Theory and algorithm","volume":"36","author":"Shi","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.displa.2026.103554_b26","article-title":"Class-incremental learning: A survey","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.displa.2026.103554_b27","doi-asserted-by":"crossref","unstructured":"J. Dong, L. Wang, Z. Fang, G. Sun, S. Xu, X. Wang, Q. Zhu, Federated class-incremental learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10164\u201310173.","DOI":"10.1109\/CVPR52688.2022.00992"},{"issue":"13","key":"10.1016\/j.displa.2026.103554_b28","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1073\/pnas.1611835114","article-title":"Overcoming catastrophic forgetting in neural networks","volume":"114","author":"Kirkpatrick","year":"2017","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.displa.2026.103554_b29","series-title":"International Conference on Machine Learning","first-page":"3987","article-title":"Continual learning through synaptic intelligence","author":"Zenke","year":"2017"},{"key":"10.1016\/j.displa.2026.103554_b30","doi-asserted-by":"crossref","unstructured":"S.A. Rebuffi, A. Kolesnikov, G. Sperl, C.H. Lampert, icarl: Incremental classifier and representation learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2001\u20132010.","DOI":"10.1109\/CVPR.2017.587"},{"key":"10.1016\/j.displa.2026.103554_b31","doi-asserted-by":"crossref","unstructured":"X. Liu, C. Wu, M. Menta, L. Herranz, B. Raducanu, A.D. Bagdanov, S. Jui, J.v. de Weijer, Generative feature replay for class-incremental learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, pp. 226\u2013227.","DOI":"10.1109\/CVPRW50498.2020.00121"},{"key":"10.1016\/j.displa.2026.103554_b32","series-title":"Progressive neural networks","author":"Rusu","year":"2016"},{"key":"10.1016\/j.displa.2026.103554_b33","series-title":"International Conference on Machine Learning","first-page":"4548","article-title":"Overcoming catastrophic forgetting with hard attention to the task","author":"Serra","year":"2018"},{"key":"10.1016\/j.displa.2026.103554_b34","series-title":"2020 IEEE International Conference on Image Processing","first-page":"1736","article-title":"Continual local training for better initialization of federated models","author":"Yao","year":"2020"},{"key":"10.1016\/j.displa.2026.103554_b35","series-title":"Federated continual learning: Concepts, challenges, and solutions","author":"Hamedi","year":"2025"},{"key":"10.1016\/j.displa.2026.103554_b36","series-title":"Federated continual learning for edge-ai: A comprehensive survey","author":"Wang","year":"2024"},{"issue":"8","key":"10.1016\/j.displa.2026.103554_b37","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.displa.2026.103554_b38","series-title":"Overcoming forgetting in federated learning on non-iid data","author":"Shoham","year":"2019"},{"key":"10.1016\/j.displa.2026.103554_b39","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.displa.2026.103554_b40","doi-asserted-by":"crossref","unstructured":"Y. Li, Q. Li, H. Wang, R. Li, W. Zhong, G. Zhang, Towards efficient replay in federated incremental learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 12820\u201312829.","DOI":"10.1109\/CVPR52733.2024.01218"},{"key":"10.1016\/j.displa.2026.103554_b41","series-title":"Adaptive federated optimization","author":"Reddi","year":"2020"},{"key":"10.1016\/j.displa.2026.103554_b42","series-title":"On the importance and applicability of pre-training for federated learning","author":"Chen","year":"2022"},{"key":"10.1016\/j.displa.2026.103554_b43","series-title":"Where to begin? on the impact of pre-training and initialization in federated learning","author":"Nguyen","year":"2022"},{"key":"10.1016\/j.displa.2026.103554_b44","doi-asserted-by":"crossref","unstructured":"C. Anderson, R. Farrell, Improving fractal pre-training, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2022, pp. 1300\u20131309.","DOI":"10.1109\/WACV51458.2022.00247"},{"key":"10.1016\/j.displa.2026.103554_b45","doi-asserted-by":"crossref","unstructured":"H. Kataoka, K. Okayasu, A. Matsumoto, E. Yamagata, R. Yamada, N. Inoue, A. Nakamura, Y. Satoh, Pre-training without natural images, in: Proceedings of the Asian Conference on Computer Vision, 2020.","DOI":"10.1007\/978-3-030-69544-6_35"},{"key":"10.1016\/j.displa.2026.103554_b46","doi-asserted-by":"crossref","unstructured":"F. Zhu, X.Y. Zhang, C. Wang, F. Yin, C.L. Liu, Prototype augmentation and self-supervision for incremental learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 5871\u20135880.","DOI":"10.1109\/CVPR46437.2021.00581"},{"key":"10.1016\/j.displa.2026.103554_b47","series-title":"Enhancing federated domain adaptation with multi-domain prototype-based federated fine-tuning","author":"Zhang","year":"2024"},{"key":"10.1016\/j.displa.2026.103554_b48","doi-asserted-by":"crossref","unstructured":"H. Cha, J. Lee, J. Shin, Co2l: Contrastive continual learning, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 9516\u20139525.","DOI":"10.1109\/ICCV48922.2021.00938"},{"key":"10.1016\/j.displa.2026.103554_b49","doi-asserted-by":"crossref","unstructured":"U. Michieli, P. Zanuttigh, Continual semantic segmentation via repulsion-attraction of sparse and disentangled latent representations, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 1114\u20131124.","DOI":"10.1109\/CVPR46437.2021.00117"},{"key":"10.1016\/j.displa.2026.103554_b50","series-title":"Sharpness-aware minimization for efficiently improving generalization","author":"Foret","year":"2020"},{"key":"10.1016\/j.displa.2026.103554_b51","series-title":"On large-batch training for deep learning: Generalization gap and sharp minima","author":"Keskar","year":"2016"},{"key":"10.1016\/j.displa.2026.103554_b52","series-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"issue":"12","key":"10.1016\/j.displa.2026.103554_b53","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 Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.displa.2026.103554_b54","doi-asserted-by":"crossref","unstructured":"Q. Wang, B. Liu, Y. Li, Traceable federated continual learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 12872\u201312881.","DOI":"10.1109\/CVPR52733.2024.01223"},{"key":"10.1016\/j.displa.2026.103554_b55","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Displays"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0141938226002179?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0141938226002179?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T07:39:01Z","timestamp":1780645141000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0141938226002179"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":55,"alternative-id":["S0141938226002179"],"URL":"https:\/\/doi.org\/10.1016\/j.displa.2026.103554","relation":{},"ISSN":["0141-9382"],"issn-type":[{"value":"0141-9382","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"AFCL-CSC: Asynchronous Federated Continual Learning with Client\u2013Server Cooperative optimization","name":"articletitle","label":"Article Title"},{"value":"Displays","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.displa.2026.103554","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":"103554"}}