The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining
City University of Hong Kong（香港城市大学）；Nanjing University of Science and Technology（南京理工大学）；Monash University（莫纳什大学）；The Hong Kong University of Science and Technology（香港科技大学）
在机器学习中，\textit{the right to be forgotten}的出现催生了一个名为\textit{machine unlearning}的范式，该范式使数据持有者能够主动从训练模型中删除其数据。现有的机器遗忘技术集中于集中式训练，其中访问所有持有者的训练数据是服务器进行遗忘过程所必须的。当无法完全访问所有训练数据时，如何实现遗忘，这在很大程度上还有待探索。一个值得注意的例子是联邦学习（FL），其中每个参与的数据持有者在本地进行训练，而不将其训练数据共享给中央服务器。本文研究了FL系统中的机器遗忘问题。我们从FL中的非学习问题的正式定义开始，并提出了一种快速的再训练方法来完全删除训练FL模型中的数据样本。由此产生的设计允许数据持有者有效地联合进行遗忘过程，同时将其训练数据保持在本地。我们的形式收敛性和复杂性分析表明，我们的设计可以保持高效率的模型效用。在四个真实世界的数据集上进行了广泛的评估，说明了我们提出的实现的有效性和性能。
英文摘要：
In Machine Learning, the emergence of \textit{the right to be forgotten} gave birth to a paradigm named \textit{machine unlearning}, which enables data holders to proactively erase their data from a trained model. Existing machine unlearning techniques focus on centralized training, where access to all holders' training data is a must for the server to conduct the unlearning process. It remains largely underexplored about how to achieve unlearning when full access to all training data becomes unavailable. One noteworthy example is Federated Learning (FL), where each participating data holder trains locally, without sharing their training data to the central server. In this paper, we investigate the problem of machine unlearning in FL systems. We start with a formal definition of the unlearning problem in FL and propose a rapid retraining approach to fully erase data samples from a trained FL model. The resulting design allows data holders to jointly conduct the unlearning process efficiently while keeping their training data locally. Our formal convergence and complexity analysis demonstrate that our design can preserve model utility with high efficiency. Extensive evaluations on four real-world datasets illustrate the effectiveness and performance of our proposed realization.
Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning
College of Computing and Data Science (CCDS), Nanyang Technological University, Singapore and Digital Trust Centre (DTC), Singapore（南洋理工大学计算与数据科学学院（CCDS）及新加坡数字信任中心（DTC））；College of Computing and Data Science (CCDS), Nanyang Technological University, Singapore（南洋理工大学计算与数据科学学院（CCDS））；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（网络与交换技术国家重点实验室，北京邮电大学）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；The Future Network of Intelligence Institute, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）未来智联网络研究院）；School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）理工学院）
Goldfish: An Efficient Federated Unlearning Framework
School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning
Department of Electrical and Computer Engineering（电气与计算机工程系）；Georgia Institute of Technology（佐治亚理工学院）；Department of Mathematics（数学系）；Massachusetts Institute of Technology（麻省理工学院）
PrivEraserVerify: Efficient, Private, and Verifiable Federated Unlearning
Department of Electronics and Communication Engineering（电子与通信工程系）；Department of Mathematics and Computer Science（数学与计算机科学系）；Khulna University of Engineering & Technology（库尔纳工程与技术大学）；Hobart and William Smith Colleges（霍巴特和威廉·斯密斯学院）；Department of Electrical and Electronic Engineering（电气与电子工程系）
Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience
Ecole Polytechnique Federale de Lausanne（洛桑联邦理工学院）；Hanoi University of Science and Technology（河内科技大学）；Griffith University（格里菲斯大学）；Humboldt-Universitat zu Berlin（柏林洪堡大学）
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）
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）
