4 papers · 1 filter
GraphDLG: Exploring Deep Leakage from Gradients in Federated Graph Learning
Shuyue Wei, Wantong Chen, Tongyu Wei +3
Federated graph learning (FGL) has recently emerged as a promising privacy-preserving paradigm that enables distributed graph learning across multiple data owners. A critical priva…
Unlocking Dynamic Inter-Client Spatial Dependencies: A Federated Spatio-Temporal Graph Learning Method for Traffic Flow Forecasting
Feng Wang, Tianxiang Chen, Shuyue Wei +4
Spatio-temporal graphs are powerful tools for modeling complex dependencies in traffic time series. However, the distributed nature of real-world traffic data across multiple stake…
Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approach
Shuyue Wei, Yongxin Tong, Zimu Zhou +2
Federated learning paradigm to utilize datasets across multiple data providers. In FL, cross-silo data providers often hesitate to share their high-quality dataset unless their dat…
Ten Challenging Problems in Federated Foundation Models
Tao Fan, Hanlin Gu, Xuemei Cao +30
Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of fed…