用差分隐私联邦学习实现疫情本地预测，既保护隐私又保持高精度。
Differentially private federated learning for localized control of infectious disease dynamics
- 在不集中数据的前提下，通过差分隐私联邦学习训练共享模型。
- 2020年11月预测误差仅26%（MAPE），R²达0.94，接近非隐私模型。
- 适合需协作又受限于隐私的疾控部门，尤其适用于疫情不同阶段。
疫情期间快速响应对控制传播至关重要。局部策略可减少资源消耗和干预影响，但本地建模常因数据不足难以实现；而数据集中又受敏感性与隐私限制。本研究以德国县级行政区及卫生机构（LHA）为对象，提出一种隐私保护的预测方法，支持公共卫生决策。基于联邦学习（FL）框架，在客户端应用差分隐私（DP），各地区仅交换梯度范数截断后的更新，并由服务器添加噪声聚合。采用多层感知机在滑动窗口上预测病例数。在新冠数据上评估：严苛隐私下预测不稳定；中等强度下表现优异——2020年11月R²约0.94（非隐私模型0.95），MAPE 26%；2022年3月R²约0.88（非隐私模型0.93），MAPE 21%。结果表明，客户端级差分隐私联邦学习可在强隐私保障下提供可用的本地预测，隐私预算需依疫情阶段调整，支持卫生机构间合规协作。
原文摘要 · Abstract (English)
In times of epidemics, swift reaction is necessary to mitigate epidemic spreading. For this reaction, localized approaches have several advantages, limiting necessary resources and reducing the impact of interventions on a larger scale. However, training a separate machine learning (ML) model on a local scale is often not feasible due to limited available data. Centralizing the data is also challenging because of its high sensitivity and privacy constraints. In this study, we consider a localized strategy based on the German counties and communities managed by the related local health authorities (LHA). For the preservation of privacy to not oppose the availability of detailed situational data, we propose a privacy-preserving forecasting method that can assist public health experts and decision makers. ML methods with federated learning (FL) train a shared model without centralizing raw data. Considering the counties, communities or LHAs as clients and finding a balance between utility and privacy, we study a FL framework with client-level differential privacy (DP). We train a shared multilayer perceptron on sliding windows of recent case counts to forecast the number of cases, while clients exchange only norm-clipped updates and the server aggregated updates with DP noise. We evaluate the approach on COVID-19 data on county-level during two phases. As expected, very strict privacy yields unstable, unusable forecasts. At a moderately strong level, the DP model closely approaches the non-DP model: R2 around 0.94 (vs. 0.95) and mean absolute percentage error (MAPE) of 26 % in November 2020; R2 around 0.88 (vs. 0.93) and MAPE of 21 % in March 2022. Overall, client-level DP-FL can deliver useful county-level predictions with strong privacy guarantees, and viable privacy budgets depend on epidemic phase, allowing privacy-compliant collaboration among health authorities for local forecasting.
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