  In this paper, we are interested in what we term the federated private
bandits framework, that combines differential privacy with multi-agent bandit
learning. We explore how differential privacy based Upper Confidence Bound
(UCB) methods can be applied to multi-agent environments, and in particular to
federated learning environments both in `master-worker' and `fully
decentralized' settings. We provide a theoretical analysis on the privacy and
regret performance of the proposed methods and explore the tradeoffs between
these two.
