One Node Per User: Node-Level Federated Learning for Graph Neural Networks

• URL: http://arxiv.org/abs/2409.19513v1
• Date: Sun, 29 Sep 2024 02:16:07 GMT
• Title: One Node Per User: Node-Level Federated Learning for Graph Neural Networks
• Authors: Zhidong Gao, Yuanxiong Guo, Yanmin Gong,
• Abstract summary: We propose a novel framework for node-level federated graph learning. We introduce a graph Laplacian term based on the feature vector's latent representation to regulate the user-side model updates.
• Score: 7.428431479479646
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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