Scalable Asynchronous Federated Modeling for Spatial Data
This work addresses privacy- and bandwidth-constrained applications like environmental monitoring by enabling scalable federated spatial modeling, though it appears incremental as it builds on existing federated and Gaussian process methods.
The paper tackles the problem of federated modeling for spatial data by addressing limitations of existing approaches that ignore spatial dependence or rely on synchronous updates, proposing an asynchronous framework based on low-rank Gaussian process approximations that achieves synchronous performance under balanced conditions and significantly outperforms it in heterogeneous settings.
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.