Accurately predicting individual-level infection state is of great value since its essential role in reducing the damage of the epidemic. However, there exists an inescapable risk of privacy leakage in the fine-grained user mobility trajectories required by individual-level infection prediction. In this article, we focus on developing a framework of privacy-preserving individual-level infection prediction based on federated learning (FL) and graph neural networks (GNN). We propose Falcon, a Federated grAph Learning method for privacy-preserving individual-level infeCtion predictiON. It utilizes a novel hypergraph structure with spatio-temporal hyperedges to describe the complex interactions between individuals and locations in the contagion process. By organically combining the FL framework with hypergraph neural networks, the information propagation process of the graph machine learning is able to be divided into two stages distributed on the server and the clients, respectively, so as to effectively protect user privacy while transmitting high-level information. Furthermore, it elaborately designs a differential privacy perturbation mechanism as well as a plausible pseudo location generation approach to preserve user privacy in the graph structure. Besides, it introduces a cooperative coupling mechanism between the individual-level prediction model and an additional region-level model to mitigate the detrimental impacts caused by the injected obfuscation mechanisms. Extensive experimental results show that our methodology outperforms state-of-the-art algorithms and is able to protect user privacy against actual privacy attacks. Our code and datasets are available at the link: https://github.com/wjfu99/FL-epidemic.

准确预测个体层面的感染状态对于减少疫情损失至关重要。然而，个体级感染预测所需的细粒度用户移动轨迹存在不可避免的隐私泄露风险。在本文中，我们重点开发基于联邦学习（FL）和图神经网络（GNN）的隐私保护个人级感染预测框架。我们提出了Falcon，一种用于保护隐私的个人级别感染预测的联合gr A ph L赚取方法。它利用具有时空超边的新颖超图结构来描述传染过程中个体和位置之间的复杂相互作用。通过将FL框架与超图神经网络有机结合，图机器学习的信息传播过程可以分为两个阶段，分别分布在服务器端和客户端，从而在传输高层信息的同时有效保护用户隐私。信息。此外，它精心设计了一种差分隐私扰动机制以及一种合理的伪位置生成方法，以在图结构中保护用户隐私。此外，它在个体级预测模型和附加区域级模型之间引入了协作耦合机制，以减轻注入的混淆机制造成的有害影响。大量的实验结果表明，我们的方法优于最先进的算法，并且能够保护用户隐私免受实际的隐私攻击。我们的代码和数据集可通过以下链接获取：https://github.com/wjfu99/FL-epidemic。