Federated learning (FL) has recently emerged as a promising distributed machine learning (ML) paradigm. Practical needs of the "right to be forgotten" and countering data poisoning attacks call for efficient techniques that can remove, or unlearn, specific training data from the trained FL model. Existing unlearning techniques in the context of ML, however, are no longer in effect for FL, mainly due to the inherent distinction in the way how FL and ML learn from data. Therefore, how to enable efficient data removal from FL models remains largely under-explored. In this paper, we take the first step to fill this gap by presenting FedEraser, the first federated unlearning methodology that can eliminate the influence of a federated client's data on the global FL model while significantly reducing the time used for constructing the unlearned FL model.The basic idea of FedEraser is to trade the central server's storage for unlearned model's construction time, where FedEraser reconstructs the unlearned model by leveraging the historical parameter updates of federated clients that have been retained at the central server during the training process of FL. A novel calibration method is further developed to calibrate the retained updates, which are further used to promptly construct the unlearned model, yielding a significant speed-up to the reconstruction of the unlearned model while maintaining the model efficacy. Experiments on four realistic datasets demonstrate the effectiveness of FedEraser, with an expected speed-up of $4\times$ compared with retraining from the scratch. We envision our work as an early step in FL towards compliance with legal and ethical criteria in a fair and transparent manner.
Federated Bayesian learning offers a principled framework for the definition of collaborative training algorithms that are able to quantify epistemic uncertainty and to produce trustworthy decisions. Upon the completion of collaborative training, an agent may decide to exercise her legal "right to be forgotten", which calls for her contribution to the jointly trained model to be deleted and discarded. This paper studies federated learning and unlearning in a decentralized network within a Bayesian framework. It specifically develops federated variational inference (VI) solutions based on the d…
Poisoning Attacks and Defenses to Federated Unlearning
ShanghaiTech University（上海科技大学）；Xidian University（西安电子科技大学）；North Carolina State University（北卡罗来纳州立大学）；University of North Texas（北德克萨斯大学）；University of Louisville（路易斯维尔大学）
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）
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）
ToFU: Transforming How Federated Learning Systems Forget User Data
School of Computer Science（计算机科学学院）；Statistics, Trinity College Dublin, Ireland（统计学，都柏林信任学院，爱尔兰）；School of Computer Science, University College Dublin, Ireland（计算机科学学院，都柏林大学学院，爱尔兰）
Zhejiang University（浙江大学）；Fudan University（复旦大学）；University of Manchester（曼彻斯特大学）；UIUC（伊利诺伊大学厄巴纳-香槟分校）
Federated learning (FL) is a collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is the implementation of the right to be forgotten (RTBF), i.e., a leaving participant has the right to request to delete its private data from the global model. However, unlearning itself may not be enough to implement RTBF unless the unlearning effect can be independently verified, an important aspect that has been overlooked in the current literature. In this paper, we prompt the concept of verifiable federated unlea…
Federated Unlearning Model Recovery in Data with Skewed Label Distributions
School of Computer Science and Technology（计算机科学与技术学院）；Dalian University of Technology（大连理工大学）；School of Engineering and Computer Science（工程与计算机科学学院）；Victoria University of Wellington（惠灵顿维多利亚大学）
National University of Defense Technology（国防科技大学）；JD Explore Academy（京东探索研究院）；The University of Sydney（悉尼大学）；Beijing Institute of Technology（北京理工大学）
Federated learning (FL) enables multiple clients to train a machine learning model collaboratively without exchanging their local data. Federated unlearning is an inverse FL process that aims to remove a specified target client's contribution in FL to satisfy the user's right to be forgotten. Most existing federated unlearning algorithms require the server to store the history of the parameter updates, which is not applicable in scenarios where the server storage resource is constrained. In this paper, we propose a simple-yet-effective subspace based federated unlearning method, dubbed SFU, th…
pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning
Saunders College of Business, Rochester Institute of Technology（罗切斯特理工学院商学院）；School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电子与计算机工程学院）
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）
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）
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）
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）
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 t…
$f$-FUM: Federated Unlearning via min--max and $f$-divergence
Department of Statistics, Universite de Geneve, Geneva, Switzerland（日内瓦大学统计系）；University of Cambridge, Cambridge, United Kingdom（剑桥大学）；The Alan Turing Institute, London, UK（艾伦·图灵研究所）
Hong Kong Polytechnic University（香港理工大学）；Dalian University of Technology（大连理工大学）
We explore the problem of selectively forgetting categories from trained CNN classification models in the federated learning (FL). Given that the data used for training cannot be accessed globally in FL, our insights probe deep into the internal influence of each channel. Through the visualization of feature maps activated by different channels, we observe that different channels have a varying contribution to different categories in image classification. Inspired by this, we propose a method for scrubbing the model clean of information about particular categories. The method does not require …
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning
University of Southern Queensland（南昆士兰大学）；Wuhan University of Technology（武汉理工大学）；Lingnan University（岭南大学）；University of Electronic Science and Technology of China（电子科技大学）；Hong Kong Polytechnic University（香港理工大学）
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）
Certified Unlearning in Decentralized Federated Learning
School of Computer Science and Technology, Shandong University, Qingdao, China（山东大学计算机科学与技术学院）；School of Electrical Engineering and Computer Science, TU Berlin, Berlin, Germany（柏林技术大学电气工程与计算机科学学院）；College of Computer Science and Technology, Ocean University of China, Qingdao, China（中国海洋大学计算机科学与技术学院）
