期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 62, 期 11, 页码 5538-5553出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2017.2690401
关键词
Algorithm design and analysis; Bayes methods; distributed algorithms; estimation; learning
资金
- National Science Foundation [CCF-1017564, CMMI-1463262]
- Office of Naval Research [N00014-12-1-0998]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1740452] Funding Source: National Science Foundation
We consider the problem of distributed learning, where a network of agents collectively aim to agree on a hypothesis that best explains a set of distributed observations of conditionally independent random processes. We propose a distributed algorithm and establish consistency, as well as a nonasymptotic, explicit, and geometric convergence rate for the concentration of the beliefs around the set of optimal hypotheses. Additionally, if the agents interact over static networks, we provide an improved learning protocol with better scalability with respect to the number of nodes in the network.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据