@inproceedings{
gao2023one,
title={One Node Per User: Node-Level Federated Learning for Graph Neural Networks},
author={Zhidong Gao and Yuanxiong Guo and Yanmin Gong},
booktitle={NeurIPS 2023 Workshop: New Frontiers in Graph Learning},
year={2023},
url={https://openreview.net/forum?id=ROB6TdsmFa}
}
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.
One Node Per User: Node-Level Federated Learning for Graph Neural Networks
Zhidong Gao Yuanxiong Guo Yanmin Gong