{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:11:08Z","timestamp":1761894668757,"version":"build-2065373602"},"reference-count":31,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"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":[[2025,6,30]]},"DOI":"10.1109\/icme59968.2025.11209918","type":"proceedings-article","created":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T17:57:42Z","timestamp":1761847062000},"page":"1-6","source":"Crossref","is-referenced-by-count":0,"title":["Mitigating Knowledge Forgetting by Generative Knowledge Replay and Forgetting-aware Aggregation in Semi-Supervised Federated Learning"],"prefix":"10.1109","author":[{"given":"Hongquan","family":"Liu","sequence":"first","affiliation":[{"name":"Fudan University,School of Computer Science,Shanghai,China"}]},{"given":"Yixin","family":"Ren","sequence":"additional","affiliation":[{"name":"Fudan University,School of Computer Science,Shanghai,China"}]},{"given":"Jihong","family":"Guan","sequence":"additional","affiliation":[{"name":"Tongji University,School of Computer Science and Technology,Shanghai,China"}]},{"given":"Shuigeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Fudan University,School of Computer Science,Shanghai,China"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics","volume":"54","author":"McMahan"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1561\/2200000083"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.1016\/j.neunet.2024.106409"},{"key":"ref4","first-page":"15","article-title":"Federated semi-supervised learning with inter-client consistency & disjoint learning","volume-title":"International Conference on Learning Representations","author":"Jeong"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1109\/ICCV51070.2023.01567"},{"key":"ref6","first-page":"17871","article-title":"Semifl: Semi-supervised federated learning for unlabeled clients with alternate training","volume":"35","author":"Diao","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref7","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proceedings of the 37th International Conference on Machine Learning","volume":"119","author":"Karimireddy"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1109\/CVPR52729.2023.00361"},{"key":"ref9","first-page":"38461","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"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1109\/BigData52589.2021.9671693"},{"year":"2018","author":"Zhao","article-title":"Federated learning with non-iid data","key":"ref11"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.1109\/CVPR52688.2022.00987"},{"doi-asserted-by":"publisher","key":"ref13","DOI":"10.1109\/ICCV51070.2023.00490"},{"key":"ref14","first-page":"12878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","volume-title":"International conference on machine learning.","author":"Zhu","year":"2021"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.1145\/3581783.3611984"},{"key":"ref16","first-page":"19767","article-title":"Revisiting weighted aggregation in federated learning with neural networks","volume-title":"International Conference on Machine Learning","author":"Li"},{"key":"ref17","first-page":"39879","article-title":"Feddisco: Federated learning with discrepancy-aware collaboration","volume-title":"International Conference on Machine Learning","author":"Ye"},{"key":"ref18","first-page":"596","article-title":"Fixmatch: Simplifying semi-supervised learning with consistency and confidence","volume":"33","author":"Sohn","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref19","first-page":"896","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Workshop on challenges in representation learning, ICML","volume":"3","author":"Lee"},{"key":"ref20","first-page":"6256","article-title":"Unsupervised data augmentation for consistency training","volume":"33","author":"Xie","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref21","first-page":"18408","article-title":"Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling","volume":"34","author":"Zhang","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref22","first-page":"20","article-title":"Freematch: Self-adaptive thresholding for semi-supervised learning","volume-title":"The Eleventh International Conference on Learning Representations","author":"Wang"},{"key":"ref23","first-page":"21","article-title":"Softmatch: Addressing the quantity-quality tradeoff in semi-supervised learning","volume-title":"Eleventh International Conference on Learning Representations","author":"Chen"},{"doi-asserted-by":"publisher","key":"ref24","DOI":"10.1109\/CVPR52688.2022.00991"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.1145\/3583780.3614991"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.1109\/CVPR52729.2023.01563"},{"year":"2021","author":"Long","article-title":"Fedcon: A contrastive framework for federated semi-supervised learning","key":"ref27"},{"doi-asserted-by":"publisher","key":"ref28","DOI":"10.1109\/CVPRW50498.2020.00359"},{"year":"2009","author":"Krizhevsky","article-title":"Learning multiple layers of features from tiny images","key":"ref29"},{"doi-asserted-by":"publisher","key":"ref30","DOI":"10.2118\/18761-MS"},{"doi-asserted-by":"publisher","key":"ref31","DOI":"10.1109\/CVPR.2016.90"}],"event":{"name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","start":{"date-parts":[[2025,6,30]]},"location":"Nantes, France","end":{"date-parts":[[2025,7,4]]}},"container-title":["2025 IEEE International Conference on Multimedia and Expo (ICME)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11208895\/11208897\/11209918.pdf?arnumber=11209918","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T05:32:58Z","timestamp":1761888778000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11209918\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,30]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/icme59968.2025.11209918","relation":{},"subject":[],"published":{"date-parts":[[2025,6,30]]}}}