用谱方法在不泄露隐私前提下提升图数据联邦学习效果
Subgraph Federated Learning via Spectral Methods
- 通过拉普拉斯平滑捕捉节点间依赖关系
- 在多个基准数据集上表现优于或媲美现有方法
- 首个具备强隐私保障的子图联邦学习方案
我们研究跨多个客户端分布的图结构数据上的联邦学习问题，尤其关注存在相互连接子图的场景，其中客户端间的连接显著影响学习过程。现有方法要么需交换敏感节点嵌入，带来隐私风险；要么依赖计算量大的步骤，难以扩展。为此，我们提出FedLap框架，利用谱域中的拉普拉斯平滑来捕获全局结构信息，有效建模节点间依赖，同时保障隐私与可扩展性。我们对FedLap的隐私性进行了形式化分析，证明其具有隐私保护能力。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。在多个基准数据集上的大量实验表明，该方法在性能上达到或超过现有技术。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。