  We consider the problem of estimating the arithmetic average of a finite
collection of real vectors stored in a distributed fashion across several
compute nodes subject to a communication budget constraint. Our analysis does
not rely on any statistical assumptions about the source of the vectors. This
problem arises as a subproblem in many applications, including reduce-all
operations within algorithms for distributed and federated optimization and
learning. We propose a flexible family of randomized algorithms exploring the
trade-off between expected communication cost and estimation error. Our family
contains the full-communication and zero-error method on one extreme, and an
$\epsilon$-bit communication and ${\cal O}\left(1/(\epsilon n)\right)$ error
method on the opposite extreme. In the special case where we communicate, in
expectation, a single bit per coordinate of each vector, we improve upon
existing results by obtaining $\mathcal{O}(r/n)$ error, where $r$ is the number
of bits used to represent a floating point value.

Distributed, Parallel, and Cluster Computing;Numerical Analysis;Machine Learning