2 papers
cs.LG2026
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…
cs.LG2024
Modeling Inter-Intra Heterogeneity for Graph Federated Learning
Wentao Yu, Shuo Chen, Yongxin Tong +2
Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the h…