# Resource-Efficient Distributed Recursive Gaussian Processes

arXiv: https://arxiv.org/abs/2609.26979 · HTML: https://mlsanity.com/paper/2609.26979/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 22 Sep 2026.

## Builds on (most cited first, 11 of 11)

- [Survey of Important Issues in UAV Communication Networks](https://mlsanity.com/paper/1603.08462/) (2016)
- [A Critical Review of Communications in Multi-Robot Systems](https://mlsanity.com/paper/2206.09484/) (2022)
- [Distributed Optimization Using the Primal-Dual Method of Multipliers](https://mlsanity.com/paper/1702.00841/) (2017)
- [Multi-Agent Safe Planning with Gaussian Processes](https://mlsanity.com/paper/2008.04452/) (2020)
- [Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification](https://mlsanity.com/paper/1902.04763/) (2019)
- [Communication efficient privacy-preserving distributed optimization using adaptive differential quantization](https://mlsanity.com/paper/2105.14416/) (2021)
- [Multi-Robot Informative Path Planning from Regression with Sparse Gaussian Processes (with Appendix)](https://mlsanity.com/paper/2309.07050/) (2023)
- [Resource-Efficient Cooperative Online Scalar Field Mapping via Distributed Sparse Gaussian Process Regression](https://mlsanity.com/paper/2309.10311/) (2023)
- [Robust, Online, and Adaptive Decentralized Gaussian Processes](https://mlsanity.com/paper/2509.18011/) (2025)
- [Communication-Efficient Distributed Kalman Filtering using ADMM](https://mlsanity.com/paper/2501.13003/) (2025)
- [Consensus-based Recursive Multi-Output Gaussian Process](https://mlsanity.com/paper/2604.10146/) (2026)

## Abstract (arXiv)

Gaussian processes (GPs) provide a flexible framework for learning unknown functions from noisy measurements while quantifying predictive uncertainty, making them well suited for estimation in multi-agent systems. However, when measurements are collected by multiple agents, maintaining a unified GP model without centralized processing requires efficient distributed algorithms that can operate using local measurements and communication with neighboring agents. In this work, we develop two distributed recursive GP (RGP) algorithms for multi-output GP regression: ADMM-RGP and PDMM-RGP. We analyze the stability and convergence of both algorithms and develop parameter selection strategies to accelerate convergence, thus reducing the communication burden. The proposed methods are validated on a real-world multi-output wind dataset, and their convergence behavior is examined across communication graphs with varying connectivity. Numerical experiments demonstrate that ADMM-RGP and PDMM-RGP can significantly reduce communication relative to the state of the art, while maintaining comparable estimation accuracy and network-wide consensus.