这篇论文提出Fed-CausalDiff，让联邦学习不仅能拟合历史数据，还能做因果干预模拟。它在四个数据集上比常规方法更准，而且通信开销可控，适合分布式医疗或金融场景。
Fed-CausalDiff是一种联邦因果扩散框架，专门用于“do-simulation”和政策评估。它将潜在状态演化分解为全局因果评分函数和局部混淆评分函数，实现解耦同步（DSS），客户端只聚合共享因果机制而保留本地特定混淆。在四个数据集上的实验显示，Fed-CausalDiff在ATE和政策价值估计精度上优于传统方法，并在通信成本与推理保真度之间取得更好平衡。
Fed-CausalDiff: Decoupled Synchronization for Federated Do-Simulation and Policy Evaluation
While federated learning enables collaborative modelling on decentralised data, standard methods merely fit historical observations. This purely observational approach is fundamentally insufficient for interventional inference and policy evaluation, as sequential actions dynamically alter future states. We propose \textbf{Fed-CausalDiff}, a federated causal diffusion framework for do-simulation. The architecture decomposes the evolution of the latent state into a global causal score function and a local confounding score function. This design enables \emph{decoupled synchronisation} (DSS), where clients aggregate only the shared causal mechanism while retaining site-specific confounders locally to handle heterogeneity. Experiments on four datasets demonstrate that Fed-CausalDiff achieves better ATE and policy-value estimation accuracy, offering a favorable trade-off between communication cost and inference fidelity.