Abstract:
In this work, we study the problem of communication-constrained distributed estimation of the mean of a random variable drawn from a location family. We assume that each ...Show MoreMetadata
Abstract:
In this work, we study the problem of communication-constrained distributed estimation of the mean of a random variable drawn from a location family. We assume that each user has an independent and identically distributed sample drawn from a continuous distribution. Each user is allowed to communicate a limited number of bits to a central server, who must estimate the true mean of the distribution with low mean squared error. Recently, an order-optimal solution was proposed for the specific case of the Gaussian distribution. We propose a similar protocol for this problem, and derive upper bounds on the achievable mean squared error for symmetric log-concave distributions.
Published in: 2023 National Conference on Communications (NCC)
Date of Conference: 23-26 February 2023
Date Added to IEEE Xplore: 21 March 2023
ISBN Information: