Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and Optimization. (arXiv:2011.12160v2 [cs.IT] UPDATED)

Communication efficient distributed mean estimation is an important primitive
that arises in many distributed learning and optimization scenarios such as
federated learning. Without any probabilistic assumptions on the underlying
data, we study the problem of distributed mean estimation where the server has
access to side information. We propose \emph{Wyner-Ziv estimators}, which are
communication and computationally efficient and near-optimal when an upper
bound for the distance between the side information and the data is known. As a
corollary, we also show …

arxiv distributed information mean optimization