Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits

• URL: http://arxiv.org/abs/2110.01463v1
• Date: Mon, 4 Oct 2021 14:01:32 GMT
• Title: Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits
• Authors: Chuanhao Li and Hongning Wang
• Abstract summary: We propose a general framework with asynchronous model update and communication for a collection of homogeneous clients and heterogeneous clients. Rigorous theoretical analysis is provided about the regret and communication cost under this distributed learning framework.
• Score: 35.47147821038291
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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