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.

翻译：本文研究了一组智能体通过流式数据协同学习共同静态潜在函数的问题。我们提出了一种轻量级分布式高斯过程回归算法，该算法充分考虑了智能体在通信、计算和存储方面的有限能力。每个智能体独立运行基于智能体的高斯过程回归，利用本地流式数据预测感兴趣的测试点；随后，智能体协同执行分布式高斯过程回归，在一组公共稀疏测试点上获得全局预测；最后，每个智能体将分布式高斯过程回归的结果与基于智能体的高斯过程回归融合，以优化其预测。通过量化预测方差和误差的瞬态与稳态性能，我们表明有限的智能体间通信在帕累托意义上提升了学习性能。通过蒙特卡罗仿真对所开发算法进行了评估。