Resource-aware Distributed Gaussian Process Regression for Real-time Machine Learning

We study the problem where a group of agents aim to collaboratively learn a common latent function through streaming data. We propose a Resource-aware Gaussian process regression algorithm that is cognizant of agents' limited capabilities in communication, computation and memory. We quantify the improvement that limited inter-agent communication brings to the transient and steady-state performance in predictive variance and predictive mean. A set of simulations is conducted to evaluate the developed algorithm.