  Contextual bandit algorithms have been recently studied under the federated
learning setting to satisfy the demand of keeping data decentralized and
pushing the learning of bandit models to the client side. But limited by the
required communication efficiency, existing solutions are restricted to linear
models to exploit their closed-form solutions for parameter estimation. Such a
restricted model choice greatly hampers these algorithms' practical utility. In
this paper, we take the first step to addressing this challenge by studying
generalized linear bandit models under the federated learning setting. We
propose a communication-efficient solution framework that employs online
regression for local update and offline regression for global update. We
rigorously proved, though the setting is more general and challenging, our
algorithm can attain sub-linear rate in both regret and communication cost,
which is also validated by our extensive empirical evaluations.
