联邦环境下用神经非参数模型精准建模稀疏事件，兼顾隐私与个性化。
Federated Neural Nonparametric Point Processes
- 在客户端融合神经嵌入与非参数点过程，灵活捕捉事件动态和不确定性。
- 通过分布级聚合机制，实现隐私保护下的全局模型更新，性能优于传统方法。
- 适合处理高隐私要求、数据稀疏的分布式事件预测场景。
时间点过程（TPPs）能有效建模时间上的事件发生，但在联邦系统中面对稀疏和不确定事件时表现不佳，且隐私问题突出。为此，我们提出联邦神经非参数点过程（FedPP）。FedPP在客户端将神经嵌入集成到逻辑高斯柯克斯过程（SGCPs）中，这是一种灵活且表达力强的TPP类，可生成高度灵活的强度函数，有效捕捉客户端特有的事件动态与不确定性，并高效压缩历史记录。在全局聚合阶段，FedPP引入基于分歧的机制，通信SGCP核超参数的分布，同时保持客户端私有参数本地化，确保隐私与个性化。大量实验表明，该方法在联邦设置下有效捕获事件不确定性和稀疏性，尤其在使用KL散度和Wasserstein距离进行全局聚合时表现更优。
原文摘要 · Abstract (English)
Temporal point processes (TPPs) are effective for modeling event occurrences over time, but they struggle with sparse and uncertain events in federated systems, where privacy is a major concern. To address this, we propose \textit{FedPP}, a Federated neural nonparametric Point Process model. FedPP integrates neural embeddings into Sigmoidal Gaussian Cox Processes (SGCPs) on the client side, which is a flexible and expressive class of TPPs, allowing it to generate highly flexible intensity functions that capture client-specific event dynamics and uncertainties while efficiently summarizing historical records. For global aggregation, FedPP introduces a divergence-based mechanism that communicates the distributions of SGCPs' kernel hyperparameters between the server and clients, while keeping client-specific parameters local to ensure privacy and personalization. FedPP effectively captures event uncertainty and sparsity, and extensive experiments demonstrate its superior performance in federated settings, particularly with KL divergence and Wasserstein distance-based global aggregation.
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