Machine unlearning aims to revoke some training data after learning in response to requests from users, model developers, and administrators. Most previous methods are based on direct fine-tuning, which may neither remove data completely nor retain full performances on the remain data. In this work, we find that, by first masking some important parameters before fine-tuning, the performances of unlearning could be significantly improved. We propose a new masking strategy tailored to unlearning based on Fisher information. Experiments on various datasets and network structures show the effectiveness of the method: without any fine-tuning, the proposed Fisher masking could unlearn almost completely while maintaining most of the performance on the remain data. It also exhibits stronger stability compared to other unlearning baselines
SAU: Sparsity-Aware Unlearning for LLMs via Gradient Masking and Importance Redistribution
School of Computer Science and Artificial Intelligence, Wuhan University of Technology（武汉理工大学计算机科学与人工智能学院）；University of California San Diego（加州大学圣地亚哥分校）
Nowadays, machine learning models, especially neural networks, become prevalent in many real-world applications.These models are trained based on a one-way trip from user data: as long as users contribute their data, there is no way to withdraw; and it is well-known that a neural network memorizes its training data. This contradicts the "right to be forgotten" clause of GDPR, potentially leading to law violations. To this end, machine unlearning becomes a popular research topic, which allows users to eliminate memorization of their private data from a trained machine learning model.In this pap…
Forget-It-All: Multi-Concept Machine Unlearning via Concept-Aware Neuron Masking
The University of Arizona（亚利桑那大学）；The University of Tulsa（塔尔萨大学）；Clemson University（克莱姆森大学）；Western Digital Corporation（西部数据公司）；University of Georgia（佐治亚大学）
Gradient Concentration, Not Weight Saliency, Explains Representation-Level Class Unlearning
DIBRIS – Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova（热那亚大学信息、生物工程、机器人与系统工程系（DIBRIS））；Centre for Defense Higher Studies (CASD)（国防高等研究中心（CASD））
FiCABU: A Fisher-Based, Context-Adaptive Machine Unlearning Processor for Edge AI
School of Intelligent Semiconductor Engineering, Chung-Ang University（智能半导体工程学院， Chung-Ang 大学）；AI Edge SoC Research Section, the Electronics and Telecommunications Research Institute（AI 边缘 SoC 研究部门，电子电信研究所）
Understanding Fine-tuning in Approximate Unlearning: A Theoretical Perspective
Department of Computer Science and Engineering（计算机科学与工程系）；State University of New York at Buffalo（纽约州立大学布法罗分校）；School of Information Science and Technology（信息科学与技术学院）；University of Science and Technology of China（中国科学技术大学）
CBD: API-Only LLM Black-Box Unlearning through Controlled Behavioral Divergence
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）；Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（北京未来区块链与隐私计算高精尖创新中心）；Department of Electronic and Electrical Engineering, Sungkyunkwan University（成均馆大学电子与电气工程系）
Unlearning Isn't Deletion: Investigating Reversibility of Machine Unlearning in LLMs
The Hong Kong Polytechnic University（香港理工大学）；Carnegie Mellon University（卡内基梅隆大学）；University of California, Santa Cruz（加州大学圣克ruz分校）；Huawei Technologies（华为技术有限公司）；Research Centre for Privacy and Security Technologies in Future Smart Systems, PolyU（未来智能系统中的隐私与安全技术研究中心，PolyU）
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）
Erase then Rectify: A Training-Free Parameter Editing Approach for Cost-Effective Graph Unlearning
Sun Yat-sen University（中山大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Guangdong Key Laboratory of Big Data Analysis and Processing（广东省大数据分析与处理重点实验室）；The Hong Kong University of Science and Technology（香港科技大学）
Data-Free Privacy-Preserving for LLMs via Model Inversion and Selective Unlearning
School of Software Technology, Zhejiang University（浙江大学软件技术学院）；Zhejiang University（浙江大学）；Hefei University of Technology（合肥工业大学）；Institute of Fundamental and Transdisciplinary Research, Zhejiang University（浙江大学基础与交叉学科研究院）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江区）区块链与数据安全研究院）；The State Key Laboratory of Blockchain and Data Security（区块链与数据安全国家重点实验室）；Zhejiang Key Laboratory of Big Data Intelligent Computing（浙江省大数据智能计算重点实验室）
