论文标题
是否在分布式统计估算中与资源限制进行协作？
To Collaborate or Not in Distributed Statistical Estimation with Resource Constraints?
论文作者
论文摘要
我们研究不同传感器/学习者收集的观察结果之间的相关性如何通过分析Fisher信息和Cramer-Rao界限来影响数据收集和协作策略。特别是，我们考虑了一个简单的设置，其中两个传感器来自双变量高斯分布，这已经激发了各种策略的采用，具体取决于两个变量与资源约束之间的相关性。我们确定了两种特殊的情况：（1）不能利用样本之间的相关性知识来进行协作估计目的；以及（2）最佳数据收集策略涉及投资稀缺的资源来进行较低的示例和传输信息，而这些信息并不立即引起人们的统计信息，并且已经知道其统计数据，其唯一的目标是提高了利益的估计性参数参数的信心。我们讨论了两个应用程序，即无线传感器网络中的IoT DDOS攻击检测和分布式估计，这可能会从我们的结果中受益。
We study how the amount of correlation between observations collected by distinct sensors/learners affects data collection and collaboration strategies by analyzing Fisher information and the Cramer-Rao bound. In particular, we consider a simple setting wherein two sensors sample from a bivariate Gaussian distribution, which already motivates the adoption of various strategies, depending on the correlation between the two variables and resource constraints. We identify two particular scenarios: (1) where the knowledge of the correlation between samples cannot be leveraged for collaborative estimation purposes and (2) where the optimal data collection strategy involves investing scarce resources to collaboratively sample and transfer information that is not of immediate interest and whose statistics are already known, with the sole goal of increasing the confidence on an estimate of the parameter of interest. We discuss two applications, IoT DDoS attack detection and distributed estimation in wireless sensor networks, that may benefit from our results.
