Epidemic prediction is of practical significance in public health, enabling early intervention, resource allocation, and strategic planning. However, privacy concerns often hinder the sharing of health data among institutions, limiting the development of accurate prediction models. In this paper, we develop a general privacy-preserving framework for node-level epidemic prediction on networks based on federated learning (FL). We frame the spatio-temporal spread of epidemics across multiple data-isolated subnetworks, where each node state represents the aggregate epidemic severity within a community. Then, both the pure temporal LSTM model and the spatio-temporal model i.e., Spatio-Temporal Graph Attention Network (STGAT) are proposed to address the federated epidemic prediction. Extensive experiments are conducted on various epidemic processes using a practical airline network, offering a comprehensive assessment of FL efficacy under diverse scenarios. By introducing the efficacy energy metric to measure system robustness under various client configurations, we systematically explore key factors influencing FL performance, including client numbers, aggregation strategies, graph partitioning, missing infectious reports. Numerical results manifest that STGAT excels in capturing spatio-temporal dependencies in dynamic processes whereas LSTM performs well in simpler pattern. Moreover, our findings highlight the importance of balancing feature consistency and volume uniformity among clients, as well as the prediction dilemma between information richness and intrinsic stochasticity of dynamic processes. This study offers practical insights into the efficacy of FL scenario in epidemic management, demonstrates the potential of FL to address broader collective dynamics.

翻译：流行病预测在公共卫生领域具有重要实践意义，能够支持早期干预、资源分配与战略规划。然而，隐私问题常阻碍机构间健康数据的共享，限制了高精度预测模型的发展。本文基于联邦学习（FL）构建了一个通用的隐私保护框架，用于网络节点级流行病预测。我们将流行病在多个数据隔离子网络间的时空传播建模为节点状态，其中每个节点状态代表社区内流行病严重程度的聚合指标。随后，我们提出了纯时序LSTM模型与时空模型——时空图注意力网络（STGAT），以解决联邦流行病预测问题。通过在实际航空网络上对多种流行病传播过程进行大量实验，全面评估了不同场景下联邦学习的效能。通过引入效能能量指标来衡量不同客户端配置下的系统鲁棒性，我们系统性地探究了影响联邦学习性能的关键因素，包括客户端数量、聚合策略、图划分方式以及感染报告缺失情况。数值结果表明，STGAT在捕捉动态过程的时空依赖性方面表现优异，而LSTM在较简单的模式中表现良好。此外，我们的研究结果强调了平衡客户端间特征一致性与数据量均匀性的重要性，以及信息丰富度与动态过程内在随机性之间的预测困境。本研究为流行病管理中的联邦学习场景效能提供了实践洞见，并展示了联邦学习在解决更广泛集体动力学问题方面的潜力。