Federated Linear Contextual Bandits with Heterogeneous Clients
Ethan Blaser Chuanhao Li Hongning Wang
The demand for collaborative and private bandit learning across multiple
agents is surging due to the growing quantity of data generated from
distributed systems. Federated bandit learning has emerged as a promising
framework for private, efficient, and decentralized online learning. However,
almost all previous works rely on strong assumptions of client homogeneity,
i.e., all participating clients shall share the same bandit model; otherwise,
they all would suffer linear regret. This greatly restricts the application of
federated bandit learning in practice. In this work, we introduce a new
approach for federated bandits for heterogeneous clients, which clusters
clients for collaborative bandit learning under the federated learning setting.
Our proposed algorithm achieves non-trivial sub-linear regret and communication
cost for all clients, subject to the communication protocol under federated
learning that at anytime only one model can be shared by the server.