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Accurately predicting individual-level infection state is of great valuesince its essential role in reducing the damage of the epidemic. However, thereexists an inescapable risk of privacy leakage in the fine-grained user mobilitytrajectories required by individual-level infection prediction. In this paper,we focus on developing a framework of privacy-preserving individual-levelinfection prediction based on federated learning (FL) and graph neural networks(GNN). We propose Falcon, a Federated grAph Learning method forprivacy-preserving individual-level infeCtion predictiON. It utilizes a novelhypergraph structure with spatio-temporal hyperedges to describe the complexinteractions between individuals and locations in the contagion process. Byorganically combining the FL framework with hypergraph neural networks, theinformation propagation process of the graph machine learning is able to bedivided into two stages distributed on the server and the clients,respectively, so as to effectively protect user privacy while transmittinghigh-level information. Furthermore, it elaborately designs a differentialprivacy perturbation mechanism as well as a plausible pseudo locationgeneration approach to preserve user privacy in the graph structure. Besides,it introduces a cooperative coupling mechanism between the individual-levelprediction model and an additional region-level model to mitigate thedetrimental impacts caused by the injected obfuscation mechanisms. Extensiveexperimental results show that our methodology outperforms state-of-the-artalgorithms 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.

Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning