Federated Unlearning (FU) aims to efficiently remove the influence of specific client data from a federated model while preserving utility for the remaining clients. However, three key challenges remain: (1) existing unlearning objectives often compromise model utility or increase vulnerability to Membership Inference Attacks (MIA); (2) there is a persistent conflict between forgetting and utility, where further unlearning inevitably harms retained performance; and (3) support for concurrent multi-client unlearning is poor, as gradient conflicts among clients degrade the quality of forgetting. To address these issues, we propose FUPareto, an efficient unlearning framework via Pareto-augmented optimization. We first introduce the Minimum Boundary Shift (MBS) Loss, which enforces unlearning by suppressing the target class logit below the highest non-target class logit; this can improve the unlearning efficiency and mitigate MIA risks. During the unlearning process, FUPareto performs Pareto improvement steps to preserve model utility and executes Pareto expansion to guarantee forgetting. Specifically, during Pareto expansion, the framework integrates a Null-Space Projected Multiple Gradient Descent Algorithm (MGDA) to decouple gradient conflicts. This enables effective, fair, and concurrent unlearning for multiple clients while minimizing utility degradation. Extensive experiments across diverse scenarios demonstrate that FUPareto consistently outperforms state-of-the-art FU methods in both unlearning efficacy and retained utility.
Train Once, Forget Precisely: Anchored Optimization for Efficient Post-Hoc Unlearning
University College London, London, United Kingdom（伦敦大学学院）；University of Nottingham, Nottingham, United Kingdom（诺丁汉大学）；Bharati Vidyapeeth College of Engineering, New Delhi, India（巴里蒂大学工程学院）
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）
Forgetting to Witness: Efficient Federated Unlearning and Its Visible Evaluation
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）
Forget by Uncertainty: Orthogonal Entropy Unlearning for Quantized Neural Networks
Hubei Key Laboratory of Transportation Internet of Things, School of Computer Science and Artificial Intelligence, Wuhan University of Technology, China（交通物联网湖北重点实验室，计算机科学与人工智能学院，武汉理工大学，中国）；College of Computing and Data Science, Nanyang Technological University, Singapore（计算与数据科学学院，南洋理工大学，新加坡）
HKUST（香港科技大学）；University of Cambridge（剑桥大学）；Central South University（中南大学）；Shanghai Jiaotong University（上海交通大学）；SenseTime Research（商汤科技研究院）；Tsinghua University（清华大学）
Decoupling the Class Label and the Target Concept in Machine Unlearning
Hong Kong Baptist University（香港浸会大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能中心）；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）；The University of Tokyo（东京大学）
FaLW: A Forgetting-aware Loss Reweighting for Long-tailed Unlearning
University of Science and Technology of China(USTC)（中国科学技术大学）；Suzhou Institute for Advanced Research, USTC（中国科学技术大学苏州研究院）；City University of Hong Kong（香港城市大学）；Tongji University（同济大学）
Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols
Department of Artificial Intelligence, Korea University, South Korea（韩国大学人工智能系）；Computer Science Dept. at Courant Institute of Mathemtical Sciences, New York University, United States（纽约大学Courant数学科学研究所计算机科学系）
CYBERCAT (Center for Cybersecurity Research of Catalonia)（加泰罗尼亚网络安全研究中心）；ComSCIAM-Center for Computational Science and Applied Mathematics（计算科学与应用数学中心）；Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili（罗维拉-维尔吉利大学计算机工程与数学系）
Bias Redistribution in Visual Machine Unlearning: Does Forgetting One Group Harm Another?
NewraLab；Beihang University（北京航空航天大学）；GoerTek Alpha Lab（歌尔Alpha实验室）；Skyline University Nigeria（尼日利亚天际线大学）；UESTC（电子科技大学）；Imperial College London（帝国理工学院）
Forgetting-MarI: LLM Unlearning via Marginal Information Regularization
Department of Mathematics, University of California Davis, USA（加州大学戴维斯分校数学系）；SLAC National Accelerator Laboratory, Stanford University, USA（斯坦福大学SLAC国家加速器实验室）；Department of Computer Science, University of California Davis, USA（加州大学戴维斯分校计算机科学系）
Quantization-Robust Unlearning through the Lens of Retain-Forget Loss Landscapes Interaction
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Pittsburgh（匹兹堡大学）；University of Arizona（亚利桑那大学）；University of Central Florida（中佛罗里达大学）
