We consider the problem of federated learning (FL) with graph-structured data distributed across multiple clients. In particular, we address the prevalent scenario of interconnected subgraphs, where interconnections between clients significantly influence the learning process. Existing approaches suffer from critical limitations, either requiring the exchange of sensitive node embeddings, thereby posing privacy risks, or relying on computationally-intensive steps, which hinders scalability. To tackle these challenges, we propose FedLap, a novel framework that leverages global structure information via Laplacian smoothing in the spectral domain to effectively capture inter-node dependencies while ensuring privacy and scalability. We provide a formal analysis of the privacy of FedLap, demonstrating that it preserves privacy. Notably, FedLap is the first subgraph FL scheme with strong privacy guarantees. Extensive experiments on benchmark datasets demonstrate that FedLap achieves competitive or superior utility compared to existing techniques.

翻译：我们研究了图结构数据分布于多个客户端的联邦学习问题。特别地，我们关注子图相互连接的普遍场景，其中客户端之间的连接关系显著影响学习过程。现有方法存在关键局限性：要么需要交换敏感的节点嵌入向量，从而带来隐私风险；要么依赖计算密集型步骤，阻碍了可扩展性。为应对这些挑战，我们提出FedLap框架，该框架通过谱域中的拉普拉斯平滑技术利用全局结构信息，在确保隐私与可扩展性的同时有效捕获节点间依赖关系。我们对FedLap的隐私性进行了形式化分析，证明其具备隐私保护能力。值得注意的是，FedLap是首个具备强隐私保证的子图联邦学习方案。在基准数据集上的大量实验表明，相较于现有技术，FedLap实现了具有竞争力或更优的性能效用。