# Scalable Asynchronous Federated Modeling for Spatial Data

arXiv: https://arxiv.org/abs/2510.01771 · HTML: https://mlsanity.com/paper/2510.01771/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 56 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Builds on (most cited first, 25 of 25)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2106.06639/) (2021)
- [Asynchronous Federated Learning on Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2109.04269/) (2021)
- [Variational Fourier features for Gaussian processes](https://mlsanity.com/paper/1611.06740/) (2016)
- [Personalized Federated Learning with Gaussian Processes](https://mlsanity.com/paper/2106.15482/) (2021)
- [A Survey on Decentralized Federated Learning](https://mlsanity.com/paper/2308.04604/) (2023)
- [Asynchronous Stochastic Coordinate Descent: Parallelism and Convergence Properties](https://mlsanity.com/paper/1403.3862/) (2014)
- [ARock: an Algorithmic Framework for Asynchronous Parallel Coordinate Updates](https://mlsanity.com/paper/1506.02396/) (2015)
- [A multi-resolution approximation for massive spatial datasets](https://mlsanity.com/paper/1507.04789/) (2015)
- [Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models](https://mlsanity.com/paper/1402.1389/) (2014)
- [Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees](https://mlsanity.com/paper/2109.04522/) (2021)
- [A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates](https://mlsanity.com/paper/2206.10189/) (2022)
- [FedFa: A Fully Asynchronous Training Paradigm for Federated Learning](https://mlsanity.com/paper/2404.11015/) (2024)
- [Global Convergence Rate of Proximal Incremental Aggregated Gradient Methods](https://mlsanity.com/paper/1608.01713/) (2016)
- [Communication-Efficient Federated Learning With Data and Client Heterogeneity](https://mlsanity.com/paper/2206.10032/) (2022)
- [Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling](https://mlsanity.com/paper/2111.14008/) (2021)
- [Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process](https://mlsanity.com/paper/2206.06357/) (2022)
- [Delay-agnostic Asynchronous Coordinate Update Algorithm](https://mlsanity.com/paper/2305.08535/) (2023)
- [Large-Scale Low-Rank Gaussian Process Prediction with Support Points](https://mlsanity.com/paper/2207.12804/) (2022)
- [Federated Automatic Latent Variable Selection in Multi-output Gaussian Processes](https://mlsanity.com/paper/2407.16935/) (2024)
- [Parallel inference for massive distributed spatial data using low-rank models](https://mlsanity.com/paper/1402.1472/) (2014)
- [GPU-Accelerated Modified Bessel Function of the Second Kind for Gaussian Processes](https://mlsanity.com/paper/2502.00356/) (2025)

## Abstract (arXiv)

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.