  Many machine learning applications require operating on a spatially
distributed dataset. Despite technological advances, privacy considerations and
communication constraints may prevent gathering the entire dataset in a central
unit. In this paper, we propose a distributed sampling scheme based on the
alternating direction method of multipliers, which is commonly used in the
optimization literature due to its fast convergence. In contrast to distributed
optimization, distributed sampling allows for uncertainty quantification in
Bayesian inference tasks. We provide both theoretical guarantees of our
algorithm's convergence and experimental evidence of its superiority to the
state-of-the-art. For our theoretical results, we use convex optimization tools
to establish a fundamental inequality on the generated local sample iterates.
This inequality enables us to show convergence of the distribution associated
with these iterates to the underlying target distribution in Wasserstein
distance. In simulation, we deploy our algorithm on linear and logistic
regression tasks and illustrate its fast convergence compared to existing
gradient-based methods.
