Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits
This work addresses communication efficiency in decentralized bandit learning, which is incremental as it adapts existing methods to federated settings.
The paper tackles the challenge of minimizing regret while reducing communication costs in federated linear contextual bandits, proposing an asynchronous framework for homogeneous and heterogeneous clients with theoretical and empirical validation.
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.