Scalable Asynchronous Federated Modeling for Spatial Data

2025-10-02

AI Summary

To address the neglect of spatial dependencies in federated learning for spatial data and the vulnerability of synchronous updates to heterogeneous communication delays, this paper proposes the first asynchronous federated modeling framework tailored for distributed spatial data. Methodologically, it introduces gradient correction and adaptive aggregation, integrated with low-rank Gaussian process approximations for block-wise optimization; only privacy-preserving summary statistics are exchanged, enabling fully asynchronous updates. Theoretically, it establishes the first convergence analysis for such a framework, explicitly characterizing how communication latency affects model performance. Experiments demonstrate that the method achieves synchronous-level accuracy under resource-homogeneous conditions and significantly outperforms existing approaches under heterogeneity—while maintaining strong robustness, high scalability, and rigorous differential privacy guarantees. The framework is validated on real-world applications including environmental monitoring and urban planning.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

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.

Problem

• Addresses spatial data modeling under privacy and communication constraints
• Overcomes limitations of synchronous updates in heterogeneous environments
• Proposes asynchronous federated learning for scalable spatial dependence modeling

Innovation

• Asynchronous federated modeling for spatial data
• Low-rank Gaussian process approximations framework
• Gradient correction and adaptive aggregation strategies