Federated learning (FL) enables distributed optimization of machine learning models while protecting privacy by independently training local models on each client and then aggregating parameters on a central server, thereby producing an effective global model. Although a variety of FL algorithms have been proposed, their training efficiency remains low when the data are not independently and identically distributed (non-i.i.d.) across different clients. We observe that the slow convergence rates of the existing methods are (at least partially) caused by the catastrophic forgetting issue during the local training stage on each individual client, which leads to a large increase in the loss function concerning the previous training data at the other clients. Here, we propose FedReg, an algorithm to accelerate FL with alleviated knowledge forgetting in the local training stage by regularizing locally trained parameters with the loss on generated pseudo data, which encode the knowledge of previous training data learned by the global model. Our comprehensive experiments demonstrate that FedReg not only significantly improves the convergence rate of FL, especially when the neural network architecture is deep and the clients' data are extremely non-i.i.d., but is also able to protect privacy better in classification problems and more robust against gradient inversion attacks. The code is available at: https://github.com/Zoesgithub/FedReg.
Overcoming Catastrophic Forgetting in Federated Class-Incremental Learning via Federated Global Twin Generator
College of Engineering & Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）；Vietnam National University - Ho Chi Minh city, University of Science（越南国家大学胡志明市分校自然科学大学）；Technische Universität Berlin（柏林工业大学）
Class-wise Federated Unlearning: Harnessing Active Forgetting with Teacher-Student Memory Generation
School of Communication Engineering, Hangzhou Dianzi University（杭州电子科技大学通信工程学院）；College of Computer Science & Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Cyberspace, Hangzhou Dianzi University（杭州电子科技大学网络空间学院）
Effective Unbounded Drift Control in Heterogeneous Federated Learning: Bayesian Fine-Tuning Using a Reference Model
School of Computer Science and Engineering（计算机科学与工程学院）；Center for Biosystems and Biotech Data Science（生物系统与生物技术数据科学中心）；Ghent University Global Campus, Incheon, Republic of Korea（因斯布鲁克大学全球校区，韩国）
Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Northwestern University（西北大学）；University of Technology Sydney（悉尼科技大学）
Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Arizona State University（亚利桑那州立大学）；University of California San Diego（加州大学圣地亚哥分校）；Purdue University（普渡大学）；University of Maryland College Park（马里兰大学学院市分校）
Onboarding Without Forgetting: Hypernetwork Personalization with Data-Free Replay for Personalized Federated Learning
VinUni-Illinois Smart Health Center（VinUni-Illinois智能健康中心）；College of Engineering & Computer Science（工程与计算机科学学院）；University of Notre Dame（诺特丹大学）；Technische Universität Berlin（柏林技术大学）
Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving
The University of Hong Kong（香港大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Southern University of Science and Technology（南方科技大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
FedCARE: Federated Unlearning with Conflict-Aware Projection and Relearning-Resistant Recovery
School of Cyber Engineering, Xidian University（电子科技大学信息工程学院）；RMIT University（皇家墨尔本理工大学）；Commonwealth Scientific and Industrial Research Organisation (CSIRO)（澳大利亚联邦科学与工业研究组织）；UNSW Sydney（新南威尔士大学悉尼分校）；University of California, Davis（加州大学戴维斯分校）
A Trust-Network-Based Federated Learning Framework for Multi-Center Aging Clock Prediction
PolyU Academy for Artificial Intelligence, The Hong Kong Polytechnic University（香港理工大学人工智能研究院）；Quantum Life；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算学系）；Kyoto University（京都大学）；Jilin University（吉林大学）
FedADB: Class Anchor-Driven Dual-Branch Federated Learning for Mitigating Forgetting
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；National Engineering Research Center of Disaster Backup and Recovery, Beijing University of Posts and Telecommunications（北京邮电大学国家灾备工程技术研究中心）；JIUTIAN Research, China Mobile（中国移动九天研究院）；School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络空间安全学院）；University of Leicester（莱斯特大学）
A Bayesian Federated Learning Framework with Online Laplace Approximation
Southern University of Science and Technology（南方科技大学）；Huazhong Agricultural University（华中农业大学）；Westlake University（西湖大学）；OPPO Research（OPPO研究院）；University of Maryland College Park（马里兰大学帕克分校）；University of Chinese Academy of Sciences（中国科学院大学）
Federated Class-Incremental Learning with New-Class Augmented Self-Distillation
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Zhongguancun Laboratory（中关村实验室）；Beijing Jiaotong University（北京交通大学）
Towards Federated Domain Unlearning: Verification Methodologies and Challenges
University of Macau（澳门大学）；University Of Electronic Science And Technology（电子科技大学）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究院）
School of Advanced Interdisciplinary Sciences, UCAS（中国科学院大学先进交叉学科学院）；MAIS, CASIA（中国科学院自动化所人工智能研究所）；School of Artificial Intelligence, UCAS（中国科学院大学人工智能学院）；Centre for Artificial Intelligence and Robotics, HKISI-CAS（香港科技大学人工智能与机器人中心，中国科学院）
FedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；School of Engineering, Hong Kong University of Science and Technology（香港科技大学工程学院）
Optimal Rate Adaption in Federated Learning with Compressed Communications
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；School of Computing, Faculty of Science and Engineering, Macquarie University（麦考瑞大学科学与工程学院计算机学院）；Peng Cheng Laboratory（鹏城实验室）；School of Computing Science, Simon Fraser University（西蒙弗雷泽大学计算科学学院）
