FedSGProx: Mitigating Data Heterogeneity and Isolated Nodes in Graph Federated Learning

Graphs capture complex node interactions and are a fundamental tool for machine learning. Graph Federated Learning (GFL) is a method that allows multiple clients to collaboratively train a global graph neural network using a federated learning framework. This approach leverages the value of distributed graph data while maintaining data privacy. Existing approaches optimize graph neural networks within the common FedAvg paradigm, but they face two problems. The first problem is data heterogeneity, which leads to variations in label distributions and subgraph structures across clients. The second problem involves isolated nodes that possess limited or no local connections. The two problems seriously degrade the performance of GFL. To address these issues, we introduce FedSGProx, a novel GFL approach. FedSGProx combines longterm and short-term constraints to mitigate local biases due to data heterogeneity. Moreover, we design a novel sampling strategy to limit the involvement of isolated nodes in local training, thereby reducing their negative impacts on local models. Empirical results show that FedSGProx achieves higher classification accuracy than existing methods, and its performance is very close to that in centralized training.