3 papers
cs.LG2025
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…
cs.LG2025
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…
cs.GT2024
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…