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：在本文中,我们研究了一组代理商试图通过流数据合作学习共同静态潜伏功能的问题。我们建议使用轻量分流的高斯进程回归(GPR)算法,认识到代理商在通信、计算和记忆方面的能力有限。每个代理商独立运行基于代理商的GPR,利用当地流数据预测测试点;然后代理商合作实施分布式GPR,以获得对一组共同的稀疏测试点的全球预测;最后,每种代理商通过分布式GPR与基于代理商的GPR连接结果,以完善其预测。通过量化在预测差异和错误方面的短暂和稳定状态性表现,我们表明有限的代理商间通信提高了Pareto意义上的学习绩效。蒙特卡洛模拟是为了评估发达的算法。