Unifying (Federated) (Private) High-Dimensional Bandits via ADMM

Apurv Shukla, Debabrota Basu

Keywords: High-dimensional bandits, ADMM, Federated Learning

Abstract: We study all possible variants of the high dimensional stochastic linear contextual bandit problem in federated and private settings. We propose a unifying algorithm design and analysis framework built on ADMM. Our method achieves existing state-of-the art guarantees in either setting for the central model. For the federated model, our results are entirely new and near-optimal in either setting. We also establish a novel lower bound on privacy-utility tradeoff for the federated model in the private setting and demonstrate on suitable numerical experiments for all problem variants.

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Track: Regular Track: unpublished work

Submission Number: 43