  The rapid proliferation of decentralized learning systems mandates the need
for differentially-private cooperative learning. In this paper, we study this
in context of the contextual linear bandit: we consider a collection of agents
cooperating to solve a common contextual bandit, while ensuring that their
communication remains private. For this problem, we devise \textsc{FedUCB}, a
multiagent private algorithm for both centralized and decentralized
(peer-to-peer) federated learning. We provide a rigorous technical analysis of
its utility in terms of regret, improving several results in cooperative bandit
learning, and provide rigorous privacy guarantees as well. Our algorithms
provide competitive performance both in terms of pseudoregret bounds and
empirical benchmark performance in various multi-agent settings.
