Lightweight Distributed Gaussian Process Regression for Online Machine Learning
This work addresses efficient online machine learning for resource-constrained agents, but it is incremental as it builds on existing Gaussian process regression methods.
The paper tackles collaborative learning of a static latent function by multiple agents with limited resources, proposing a lightweight distributed Gaussian process regression algorithm that fuses local and global predictions. It shows that limited inter-agent communication improves predictive variance and error in Pareto sense, with evaluations via Monte Carlo simulation.
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.