提出异步联邦空间建模框架，解决数据隐私与通信瓶颈下的高精度污染预测问题。
Scalable Asynchronous Federated Modeling for Spatial Data
- 基于低秩高斯过程近似，采用分块优化与梯度修正策略实现异步更新。
- 理论证明线性收敛性，且收敛速度受延迟影响可量化。
- 在资源不均场景下显著优于同步方法，适合真实环境中的分布式传感器网络。
空间数据在环境监测和城市规划中至关重要，但通常分布在多个设备上，受隐私与通信限制难以直接共享。联邦建模提供了保护隐私的全局建模方案。例如，环境传感器网络受限于隐私与带宽，需仅共享隐私保护的摘要信息，以生成无需集中原始数据的高分辨率污染地图。然而，现有方法或忽略空间相关性，或依赖同步更新，在异构环境中易受慢节点拖累。本文提出一种基于低秩高斯过程近似的异步联邦建模框架，采用分块优化，并引入梯度修正、自适应聚合与稳定更新策略。理论上，建立了显式依赖延迟的线性收敛性，具有独立理论意义。数值实验表明，该异步算法在资源均衡时达到同步性能，而在异构环境下显著超越同步方法，展现出更强鲁棒性与可扩展性。
原文摘要 · Abstract (English)
Spatial data are central to applications such as environmental monitoring and urban planning, but are often distributed across devices where privacy and communication constraints limit direct sharing. Federated modeling offers a practical solution that preserves data privacy while enabling global modeling across distributed data sources. For instance, environmental sensor networks are privacy- and bandwidth-constrained, motivating federated spatial modeling that shares only privacy-preserving summaries to produce timely, high-resolution pollution maps without centralizing raw data. However, existing federated modeling approaches either ignore spatial dependence or rely on synchronous updates that suffer from stragglers in heterogeneous environments. This work proposes an asynchronous federated modeling framework for spatial data based on low-rank Gaussian process approximations. The method employs block-wise optimization and introduces strategies for gradient correction, adaptive aggregation, and stabilized updates. We establish linear convergence with explicit dependence on staleness, a result of standalone theoretical significance. Moreover, numerical experiments demonstrate that the asynchronous algorithm achieves synchronous performance under balanced resource allocation and significantly outperforms it in heterogeneous settings, showcasing superior robustness and scalability.
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