Lightweight Distributed Gaussian Process Regression for Online Machine Learning
Abstract
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.
Cite
@article{arxiv.2105.04738,
title = {Lightweight Distributed Gaussian Process Regression for Online Machine Learning},
author = {Zhenyuan Yuan and Minghui Zhu},
journal= {arXiv preprint arXiv:2105.04738},
year = {2023}
}