{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:02:31Z","timestamp":1777568551440,"version":"3.51.4"},"reference-count":50,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,15]]},"DOI":"10.1109\/bigdata62323.2024.10825220","type":"proceedings-article","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T18:31:23Z","timestamp":1737052283000},"page":"3692-3701","source":"Crossref","is-referenced-by-count":6,"title":["FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework"],"prefix":"10.1109","author":[{"given":"Yuchen","family":"He","sequence":"first","affiliation":[{"name":"East China Normal University,School of Computer Science and Technology,Shanghai,China,200062"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuyun","family":"Shen","sequence":"additional","affiliation":[{"name":"East China Normal University,School of Computer Science and Technology,Shanghai,China,200062"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Formal-Tech Information Technology Co., Lt,Shanghai,China,200062"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Jin","sequence":"additional","affiliation":[{"name":"TongJi University,School of Software Engineering,Shanghai,China,200092"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Fed-CPrompt: Contrastive prompt for rehearsal-free federated continual learning","author":"Bagwe","year":"2023","journal-title":"Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01252-6_33"},{"key":"ref3","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"ICML","author":"Chen"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3057446"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref6","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","volume-title":"NAACL","author":"Devlin"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00992"},{"key":"ref8","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","volume-title":"ICLR","author":"Dosovitskiy"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.08.002"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00823"},{"key":"ref13","article-title":"Distilling the knowledge in a neural network","author":"Hinton","year":"2015"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00990"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01565"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2024.3418862"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref19","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"CoRR"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"ref21","author":"Krizhevsky","year":"2009","journal-title":"Learning multiple layers of features from tiny images"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.353"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/443"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2022.3213473"},{"key":"ref29","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"AISTATS","author":"McMahan"},{"key":"ref30","article-title":"Better generative replay for continual federated learning","volume-title":"ICLR","author":"Qi"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01146"},{"key":"ref33","article-title":"A closer look at rehearsalfree continual learning","volume-title":"CVPR","author":"Smith"},{"key":"ref34","article-title":"Prototypical networks for few-shot learning","volume-title":"NeurIPS","author":"Snell"},{"key":"ref35","article-title":"Federated learning from pre-trained models: A contrastive learning approach","volume-title":"NeurIPS","author":"Tan"},{"key":"ref36","article-title":"Attention is all you need","volume-title":"NeurIPS","author":"Vaswani"},{"key":"ref37","author":"Wah","year":"2011","journal-title":"The caltech-ucsd birds-200 - 2011 dataset"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2024.3367329"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19809-0_36"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00024"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00046"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00303"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref44","article-title":"Federated continual learning with weighted inter-client transfer","volume-title":"ICML","author":"Yoon"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01754"},{"key":"ref46","article-title":"Federated learning with label distribution skew via logits calibration","volume-title":"ICML","author":"Zhang"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00441"},{"key":"ref48","author":"Zhao","year":"2018","journal-title":"Federated learning with non-iid data"},{"key":"ref49","article-title":"A model or 603 exemplars: Towards memory-efficient class-incremental learning","volume-title":"ICLR","author":"Zhou"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2024\/924"}],"event":{"name":"2024 IEEE International Conference on Big Data (BigData)","location":"Washington, DC, USA","start":{"date-parts":[[2024,12,15]]},"end":{"date-parts":[[2024,12,18]]}},"container-title":["2024 IEEE International Conference on Big Data (BigData)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10824975\/10824942\/10825220.pdf?arnumber=10825220","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T07:45:41Z","timestamp":1737099941000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10825220\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,15]]},"references-count":50,"URL":"https:\/\/doi.org\/10.1109\/bigdata62323.2024.10825220","relation":{},"subject":[],"published":{"date-parts":[[2024,12,15]]}}}