分布式估计与学习在异构网络中的应用
Distributed Estimation and Learning over Heterogeneous Networks
AI总结：
研究网络代理在不确定变量下进行决策时的估计与学习问题，提出高效处理数据异质性和时间异质性的方法，通过聚合方案结合分布式数据，实现全局高效估计。
摘要：
研究网络代理在不确定变量下进行决策时的估计与学习问题，提出高效处理数据异质性和时间异质性的方法，通过聚合方案结合分布式数据，实现全局高效估计。
英文摘要：
We consider several estimation and learning problems that networked agents face when making decisions given their uncertainty about an unknown variable. Our methods are designed to efficiently deal with heterogeneity in both size and quality of the observed data, as well as heterogeneity over time (intermittence). The goal of the studied aggregation schemes is to efficiently combine the observed data that is spread over time and across several network nodes, accounting for all the network heterogeneities. Moreover, we require no form of coordination beyond the local neighborhood of every network agent or sensor node. The three problems that we consider are (i) maximum likelihood estimation of the unknown given initial data sets, (ii) learning the true model parameter from streams of data that the agents receive intermittently over time, and (iii) minimum variance estimation of a complete sufficient statistic from several data points that the networked agents collect over time. In each case we rely on an aggregation scheme to combine the observations of all agents; moreover, when the agents receive streams of data over time, we modify the update rules to accommodate the most recent observations. In every case, we demonstrate the efficiency of our algorithms by proving convergence to the globally efficient estimators given the observations of all agents. We supplement these results by investigating the rate of convergence and providing finite-time performance guarantees.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
