6 papers
FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learning
Tao Liu, Xuehe Wang
Federated learning has become a popular paradigm for privacy protection and edge-based machine learning. However, defending against differential attacks and devising incentive stra…
Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing
Wenhao Yuan, Chenchen Lin, Jian Chen +3
In large language model (LLM) agents, reasoning trajectories are treated as reliable internal beliefs for guiding actions and updating memory. However, coherent reasoning can still…
Chain-of-Context Learning: Dynamic Constraint Understanding for Multi-Task VRPs
Shuangchun Gui, Suyu Liu, Xuehe Wang +1
Multi-task Vehicle Routing Problems (VRPs) aim to minimize routing costs while satisfying diverse constraints. Existing solvers typically adopt a unified reinforcement learning (RL…
Degree of Staleness-Aware Data Updating in Federated Learning
Tao Liu, Xuehe Wang
Handling data staleness remains a significant challenge in federated learning with highly time-sensitive tasks, where data is generated continuously and data staleness largely affe…
Multi-Hop Privacy Propagation for Differentially Private Federated Learning in Social Networks
Chenchen Lin, Xuehe Wang
Federated learning (FL) enables collaborative model training across decentralized clients without sharing local data, thereby enhancing privacy and facilitating collaboration among…
A Game-Theoretic Framework for Privacy-Aware Client Sampling in Federated Learning
Wenhao Yuan, Xuehe Wang
This paper aims to design a Privacy-aware Client Sampling framework in Federated learning, named FedPCS, to tackle the heterogeneous client sampling issues and improve model perfor…