Universal Quantile Estimation with Feedback in the Communication-Constrained Setting

Abstract

We consider the following problem of decentralized statistical inference: given i.i.d. samples from an unknown distribution, estimate an arbitrary quantile subject to limits on the number of bits exchanged. We analyze a standard fusion-based architecture, in which each of $m$ sensors transmits a single bit to the fusion center, which in turn is permitted to send some number $k$ bits of feedback. Supposing that each of $\nodenum$ sensors receives $n$ observations, the optimal centralized protocol yields mean-squared error decaying as $\order(1/[n m])$. We develop and analyze the performance of various decentralized protocols in comparison to this centralized gold-standard. First, we describe a decentralized protocol based on $k = \log(\nodenum)$ bits of feedback that is strongly consistent, and achieves the same asymptotic MSE as the centralized optimum. Second, we describe and analyze a decentralized protocol based on only a single bit ($k=1$) of feedback. For step sizes independent of $m$, it achieves an asymptotic MSE of order $\order[1/(n \sqrt{m})]$, whereas for step sizes decaying as $1/\sqrt{m}$, it achieves the same $\order(1/[n m])$ decay in MSE as the centralized optimum. Our theoretical results are complemented by simulations, illustrating the tradeoffs between these different protocols.