  Linear contextual bandit is a popular online learning problem. It has been
mostly studied in centralized learning settings. With the surging demand of
large-scale decentralized model learning, e.g., federated learning, how to
retain regret minimization while reducing communication cost becomes an open
challenge. In this paper, we study linear contextual bandit in a federated
learning setting. We propose a general framework with asynchronous model update
and communication for a collection of homogeneous clients and heterogeneous
clients, respectively. Rigorous theoretical analysis is provided about the
regret and communication cost under this distributed learning framework; and
extensive empirical evaluations demonstrate the effectiveness of our solution.
