  This paper introduces Distributed Stein Variational Gradient Descent (DSVGD),
a non-parametric generalized Bayesian inference framework for federated
learning. DSVGD maintains a number of non-random and interacting particles at a
central server to represent the current iterate of the model global posterior.
The particles are iteratively downloaded and updated by one of the agents with
the end goal of minimizing the global free energy. By varying the number of
particles, DSVGD enables a flexible trade-off between per-iteration
communication load and number of communication rounds. DSVGD is shown to
compare favorably to benchmark frequentist and Bayesian federated learning
strategies, also scheduling a single device per iteration, in terms of accuracy
and scalability with respect to the number of agents, while also providing
well-calibrated, and hence trustworthy, predictions.
