{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:47:40Z","timestamp":1767340060391,"version":"3.28.0"},"reference-count":37,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:00:00Z","timestamp":1721001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:00:00Z","timestamp":1721001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,15]]},"DOI":"10.1109\/icme57554.2024.10687881","type":"proceedings-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T17:24:16Z","timestamp":1727717056000},"page":"1-6","source":"Crossref","is-referenced-by-count":1,"title":["FedFRR: Federated Forgetting-Resistant Representation Learning"],"prefix":"10.1109","author":[{"given":"Hui","family":"Wang","sequence":"first","affiliation":[{"name":"Beihang University,School of Computer Science and Engineering,Beijing,China"}]},{"given":"Jie","family":"Sun","sequence":"additional","affiliation":[{"name":"Zhongguancun Laboratory,Beijing,China"}]},{"given":"Tianyu","family":"Wo","sequence":"additional","affiliation":[{"name":"Beihang University Zhongguancun Laboratory,School of Software,Beijing,China"}]},{"given":"Xudong","family":"Liu","sequence":"additional","affiliation":[{"name":"Beihang University Zhongguancun Laboratory,School of Computer Science and Engineering,Beijing,China"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Artificial intelligence and statistics.","author":"McMahan","year":"2017"},{"key":"ref2","first-page":"25 123","article-title":"Learning debiased representation via disentangled feature augmentation","volume":"34","author":"Lee","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref3","article-title":"Overcoming catastrophic forgetting by incremental moment matching","volume":"30","author":"Lee","year":"2017","journal-title":"NeurlIPS"},{"key":"ref4","article-title":"Lifelong learning with dynamically expandable networks","author":"Yoon","year":"2018","journal-title":"ICLR"},{"article-title":"Multi-agent distributed lifelong learning for collective knowledge acquisition","year":"2018","author":"Rostami","key":"ref5"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3489517.3530578"},{"article-title":"Unsupervised representation learning with deep convolutional gans","year":"2015","author":"Radford","key":"ref7"},{"key":"ref8","article-title":"Unsupervised representation learning by predicting image rotations","author":"Gidaris","year":"2018","journal-title":"ICLR"},{"key":"ref9","first-page":"9929","article-title":"Understanding contrastive representation learning through alignment and uniformity on the hypersphere","volume-title":"ICML.","author":"Wang","year":"2020"},{"key":"ref10","article-title":"mixup: Beyond empirical risk minimization","author":"Zhang","year":"2018","journal-title":"ICLR"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref12","first-page":"4401","article-title":"A style-based generator architecture for gans","author":"Karras","year":"2019","journal-title":"CVPR"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref13"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref14"},{"key":"ref15","first-page":"12 073","article-title":"Federated continual learning with weighted inter-client transfer","volume-title":"ICML.","author":"Yoon","year":"2021"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2014.7025068"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref18","first-page":"15 920","article-title":"Dark experience for general continual learning: a strong, simple baseline","volume":"33","author":"Buzzega","year":"2020","journal-title":"NeurlIPS"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"ref20","article-title":"Representation learning via invariant causal mechanisms","author":"Mitrovic","year":"2020","journal-title":"ICLR"},{"key":"ref21","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"ICML.","author":"Chen","year":"2020"},{"article-title":"Federated unsupervised representation learning","year":"2020","author":"Zhang","key":"ref22"},{"key":"ref23","first-page":"12 073","article-title":"Federated continual learning with weighted inter-client transfer","volume-title":"ICML.","author":"Yoon","year":"2021"},{"article-title":"Feddar: Federated domain-aware representation learning","year":"2022","author":"Zhong","key":"ref24"},{"key":"ref25","first-page":"7308","article-title":"Understanding the role of training regimes in continual learning","volume":"33","author":"Mirzadeh","year":"2020","journal-title":"NeurlIPS"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"journal-title":"ICLR.","article-title":"Efficient lifelong learning with a-gem","author":"Chaudhry","key":"ref27"},{"key":"ref28","article-title":"Scalable and order-robust continual learning with additive parameter decomposition","volume-title":"ICLR.","author":"Yoon","year":"2020"},{"key":"ref29","article-title":"Reinforced continual learning","volume":"31","author":"Xu","year":"2018","journal-title":"NeurlIPS"},{"journal-title":"ICLR.","article-title":"Functional regularisation for continual learning with gaussian processes","author":"Titsias","key":"ref30"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i2.16255"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-30164-8_251"},{"issue":"11","key":"ref33","article-title":"Visualizing data using t-sne","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"Journal of machine learning research"},{"volume-title":"Information theory and statistics.","year":"1997","author":"Kullback","key":"ref34"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01080"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977653.ch78"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10095724"}],"event":{"name":"2024 IEEE International Conference on Multimedia and Expo (ICME)","start":{"date-parts":[[2024,7,15]]},"location":"Niagara Falls, ON, Canada","end":{"date-parts":[[2024,7,19]]}},"container-title":["2024 IEEE International Conference on Multimedia and Expo (ICME)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10685847\/10687354\/10687881.pdf?arnumber=10687881","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T05:56:45Z","timestamp":1727762205000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10687881\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,15]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/icme57554.2024.10687881","relation":{},"subject":[],"published":{"date-parts":[[2024,7,15]]}}}