Federated Online Sparse Decision Making

• URL: http://arxiv.org/abs/2202.13448v1
• Date: Sun, 27 Feb 2022 20:34:41 GMT
• Title: Federated Online Sparse Decision Making
• Authors: Chi-Hua Wang, Wenjie Li, Guang Cheng, and Guang Lin
• Abstract summary: textttFedego Lasso relies on a novel multi-client-selfish bandit policy design. Experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real-world datasets.
• Score: 24.856596181768364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a novel federated linear contextual bandits model, where individual clients face different K-armed stochastic bandits with high-dimensional decision context and coupled through common global parameters. By leveraging the sparsity structure of the linear reward , a collaborative algorithm named \texttt{Fedego Lasso} is proposed to cope with the heterogeneity across clients without exchanging local decision context vectors or raw reward data. \texttt{Fedego Lasso} relies on a novel multi-client teamwork-selfish bandit policy design, and achieves near-optimal regrets for shared parameter cases with logarithmic communication costs. In addition, a new conceptual tool called federated-egocentric policies is introduced to delineate exploration-exploitation trade-off. Experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real-world datasets.

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