5 papers
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…
The End of Manual Decoding: Towards Truly End-to-End Language Models
Zhichao Wang, Dongyang Ma, Xinting Huang +6
The "end-to-end" label for LLMs is a misnomer. In practice, they depend on a non-differentiable decoding process that requires laborious, hand-tuning of hyperparameters like temper…
LMM-Incentive: Large Multimodal Model-based Incentive Design for User-Generated Content in Web 3.0
Jinbo Wen, Jiawen Kang, Linfeng Zhang +5
Web 3.0 represents the next generation of the Internet, which is widely recognized as a decentralized ecosystem that focuses on value expression and data ownership. By leveraging b…
Fairness-aware Anomaly Detection via Fair Projection
Feng Xiao, Xiaoying Tang, Jicong Fan
Unsupervised anomaly detection is a critical task in many high-social-impact applications such as finance, healthcare, social media, and cybersecurity, where demographics involving…
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…