On Communication Cost of Distributed Statistical Estimation and Dimensionality

TL;DR

This paper investigates the relationship between dimensionality and communication cost in distributed Gaussian mean estimation, establishing lower bounds and proposing protocols that optimize communication efficiency.

Contribution

It provides new lower bounds on communication costs for distributed estimation and introduces protocols that approach these bounds, especially for structured sparse parameters.

Findings

Communication cost scales linearly with dimensions.

New lower bounds of a(md/log(m)) and a(md) bits for interactive and simultaneous settings.

An interactive protocol achieving minimax squared loss with O(md) bits.

Abstract

We explore the connection between dimensionality and communication cost in distributed learning problems. Specifically we study the problem of estimating the mean $\vec{\theta}$ of an unknown $d$ dimensional gaussian distribution in the distributed setting. In this problem, the samples from the unknown distribution are distributed among $m$ different machines. The goal is to estimate the mean $\vec{\theta}$ at the optimal minimax rate while communicating as few bits as possible. We show that in this setting, the communication cost scales linearly in the number of dimensions i.e. one needs to deal with different dimensions individually. Applying this result to previous lower bounds for one dimension in the interactive setting \cite{ZDJW13} and to our improved bounds for the simultaneous setting, we prove new lower bounds of $\Omega(md/\log(m))$ and $\Omega(md)$ for the bits of communication needed to achieve the minimax squared loss, in the interactive and simultaneous settings respectively. To complement, we also demonstrate an interactive protocol achieving the minimax squared loss with $O(md)$ bits of communication, which improves upon the simple simultaneous protocol by a logarithmic factor. Given the strong lower bounds in the general setting, we initiate the study of the distributed parameter estimation problems with structured parameters. Specifically, when the parameter is promised to be $s$-sparse, we show a simple thresholding based protocol that achieves the same squared loss while saving a $d/s$ factor of communication. We conjecture that the tradeoff between communication and squared loss demonstrated by this protocol is essentially optimal up to logarithmic factor.