One Node Per User: Node-Level Federated Learning for Graph Neural Networks
<|reference_start|>One Node Per User: Node-Level Federated Learning for Graph Neural Networks: 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.<|reference_end|>
@article{gao2024one,
    title={One Node Per User: Node-Level Federated Learning for Graph Neural
  Networks},
    author={Zhidong Gao, Yuanxiong Guo, Yanmin Gong},
    journal={arXiv preprint arXiv:2409.19513},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19513},
    primaryClass={cs.LG cs.AI}
}