3 papers
cs.LG2026
FUPareto: Bridging the Forgetting-Utility Gap in Federated Unlearning via Pareto Augmented Optimization
Zeyan Wang, Zhengmao Liu, Yongxin Cai +5
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…
cs.CL2025
Multi-Objective Large Language Model Unlearning
Zibin Pan, Shuwen Zhang, Yuesheng Zheng +3
Machine unlearning in the domain of large language models (LLMs) has attracted great attention recently, which aims to effectively eliminate undesirable behaviors from LLMs without…
cs.LG2024
Federated Unlearning with Gradient Descent and Conflict Mitigation
Zibin Pan, Zhichao Wang, Chi Li +4
Federated Learning (FL) has received much attention in recent years. However, although clients are not required to share their data in FL, the global model itself can implicitly re…