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.
翻译:我们研究的是一组代理商试图通过流数据合作学习共同潜在功能的问题。我们建议了一种意识到代理商通信、计算和记忆能力有限的资源觉悟高斯进程回归算法。我们量化了有限的代理商通信给预测差异和预测平均值的短暂和稳定状态表现带来的改进。我们进行了一系列模拟,以评价开发的算法。