Federated Graph Learning (FGL) has emerged as a promising way to learn high-quality representations from distributed graph data with privacy preservation. Despite considerable efforts have been made for FGL under either cross-device or cross-silo paradigm, how to effectively capture graph knowledge in a more complicated cross-silo cross-device environment remains an under-explored problem. However, this task is challenging because of the inherent hierarchy and heterogeneity of decentralized clients, diversified privacy constraints in different clients, and the cross-client graph integrity requirement. To this end, in this paper, we propose a Hierarchical Federated Graph Learning (HiFGL) framework for cross-silo cross-device FGL. Specifically, we devise a unified hierarchical architecture to safeguard federated GNN training on heterogeneous clients while ensuring graph integrity. Moreover, we propose a Secret Message Passing (SecMP) scheme to shield unauthorized access to subgraph-level and node-level sensitive information simultaneously. Theoretical analysis proves that HiFGL achieves multi-level privacy preservation with complexity guarantees. Extensive experiments on real-world datasets validate the superiority of the proposed framework against several baselines. Furthermore, HiFGL's versatile nature allows for its application in either solely cross-silo or cross-device settings, further broadening its utility in real-world FGL applications.

翻译：联邦图学习（FGL）已成为一种在隐私保护前提下从分布式图数据中学习高质量表征的有前景的方法。尽管已有大量研究致力于在跨设备或跨孤岛范式下进行FGL，但如何在更复杂的跨孤岛跨设备环境中有效捕获图知识仍是一个尚未充分探索的问题。然而，该任务具有挑战性，原因在于去中心化客户端固有的层次性与异构性、不同客户端多样化的隐私约束以及跨客户端的图完整性要求。为此，本文提出了一种面向跨孤岛跨设备FGL的层次化联邦图学习（HiFGL）框架。具体而言，我们设计了一种统一的层次化架构，以保障异构客户端上的联邦图神经网络训练，同时确保图完整性。此外，我们提出了一种秘密消息传递（SecMP）方案，以同时防止对子图级和节点级敏感信息的未授权访问。理论分析证明，HiFGL在保证复杂度的情况下实现了多级隐私保护。在真实数据集上的大量实验验证了所提框架相对于多种基线的优越性。此外，HiFGL的通用性使其可单独应用于纯跨孤岛或跨设备场景，进一步拓宽了其在现实世界FGL应用中的实用性。