Communication Efficient Federated Learning for Generalized Linear Bandits

Understand

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.