One Node Per User: Node-Level Federated Learning for Graph Neural Networks
This addresses privacy issues for users in graph-based applications, but it is incremental as it builds on existing federated learning and GNN methods.
The paper tackles the problem of privacy concerns in Graph Neural Networks (GNNs) training by proposing a node-level federated learning framework that decouples message-passing and feature transformation, achieving better performance on multiple datasets compared to baselines.
Graph Neural Networks (GNNs) training often necessitates gathering raw user data on a central server, which raises significant privacy concerns. Federated learning emerges as a solution, enabling collaborative model training without users directly sharing their raw data. However, integrating federated learning with GNNs presents unique challenges, especially when a client represents a graph node and holds merely a single feature vector. In this paper, we propose a novel framework for node-level federated graph learning. Specifically, we decouple the message-passing and feature vector transformation processes of the first GNN layer, allowing them to be executed separately on the user devices and the cloud server. Moreover, we introduce a graph Laplacian term based on the feature vector's latent representation to regulate the user-side model updates. The experiment results on multiple datasets show that our approach achieves better performance compared with baselines.