论文标题
有限样本保证分布式在线参数估计和通信成本
Finite Sample Guarantees for Distributed Online Parameter Estimation with Communication Costs
论文作者
论文摘要
我们研究以分布式和在线方式估算未知参数的问题。现有在分布式在线学习上的工作通常专注于渐近分析，或者为后悔提供界限。但是，这些结果可能不会直接转化为有限的时间段数后学习模型的误差的界限。在本文中，我们提出了一种分布式的在线估计算法，该算法使网络中的每个代理可以通过与邻居进行通信来提高其估计准确性。我们在估计误差上提供了非反应界限，利用了基础模型的统计特性。我们的分析表明，估计错误与通信成本之间的权衡。此外，我们的分析使我们能够确定可以停止通信的时间（由于与通信相关的成本），同时达到所需的估计准确性。我们还提供了一个数值示例来验证我们的结果。
We study the problem of estimating an unknown parameter in a distributed and online manner. Existing work on distributed online learning typically either focuses on asymptotic analysis, or provides bounds on regret. However, these results may not directly translate into bounds on the error of the learned model after a finite number of time-steps. In this paper, we propose a distributed online estimation algorithm which enables each agent in a network to improve its estimation accuracy by communicating with neighbors. We provide non-asymptotic bounds on the estimation error, leveraging the statistical properties of the underlying model. Our analysis demonstrates a trade-off between estimation error and communication costs. Further, our analysis allows us to determine a time at which the communication can be stopped (due to the costs associated with communications), while meeting a desired estimation accuracy. We also provide a numerical example to validate our results.
