# Lightweight Distributed Gaussian Process Regression for Online Machine Learning

arXiv: https://arxiv.org/abs/2105.04738 · HTML: https://mlsanity.com/paper/2105.04738/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 55 references that are in the map.
- First posted on arXiv 11 May 2021.

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

- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [When Gaussian Process Meets Big Data: A Review of Scalable GPs](https://mlsanity.com/paper/1807.01065/) (2018)
- [Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets](https://mlsanity.com/paper/1406.7343/) (2014)
- [Rates of Convergence for Sparse Variational Gaussian Process Regression](https://mlsanity.com/paper/1903.03571/) (2019)
- [Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs](https://mlsanity.com/paper/1709.06764/) (2017)
- [Safe Learning of Quadrotor Dynamics Using Barrier Certificates](https://mlsanity.com/paper/1710.05472/) (2017)
- [Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression](https://mlsanity.com/paper/1806.00720/) (2018)
- [Learning of Gaussian Processes in Distributed and Communication Limited Systems](https://mlsanity.com/paper/1705.02627/) (2017)
- [Online semi-parametric learning for inverse dynamics modeling](https://mlsanity.com/paper/1603.05412/) (2016)
- [Data-Driven Distributed State Estimation and Behavior Modeling in Sensor Networks](https://mlsanity.com/paper/2009.10827/) (2020)
- [Anytime computation algorithms for approach-evasion differential games](https://mlsanity.com/paper/1308.1174/) (2013)

## Built on by (most cited first, 4 of 4)

- [Streaming Generated Gaussian Process Experts for Online Learning and Control: Extended Version](https://mlsanity.com/paper/2508.03679/) (2025)
- [Quality or Quantity? Error-Informed Selective Online Learning with Gaussian Processes in Multi-Agent Systems: Extended Version](https://mlsanity.com/paper/2601.14275/) (2026)
- [Bayesian meta learning for trustworthy uncertainty quantification](https://mlsanity.com/paper/2407.19287/) (2024)
- [Byzantine-resilient federated online learning for Gaussian process regression](https://mlsanity.com/paper/2507.14021/) (2025)

## Abstract (arXiv)

In this paper, we study the problem where a group of agents aim to collaboratively learn a common static latent function through streaming data. We propose a lightweight distributed Gaussian process regression (GPR) algorithm that is cognizant of agents' limited capabilities in communication, computation and memory. Each agent independently runs agent-based GPR using local streaming data to predict test points of interest; then the agents collaboratively execute distributed GPR to obtain global predictions over a common sparse set of test points; finally, each agent fuses results from distributed GPR with agent-based GPR to refine its predictions. By quantifying the transient and steady-state performances in predictive variance and error, we show that limited inter-agent communication improves learning performances in the sense of Pareto. Monte Carlo simulation is conducted to evaluate the developed algorithm.