Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph
Learning
Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph
Learning
Federated Graph Learning (FGL) enables multiple clients to jointly train powerful graph learning models, e.g., Graph Neural Networks (GNNs), without sharing their local graph data for graph-related downstream tasks, such as graph property prediction. In the real world, however, the graph data can suffer from significant distribution shifts across clients …