Federated High-Dimensional Online Decision Making

Chi-Hua Wang, Wenjie Li, Guang Lin

Abstract:

We resolve the main challenge of federated bandit policy design via exploration-exploitation
trade-off delineation under data decentralization with a local privacy protection argument.
Such a challenge is practical in domain-specific applications and admits another layer of
complexity in applications of medical decision-making and web marketing, where high-
dimensional decision contexts are sensitive but important to inform decision-making. Exist-
ing (low dimensional) federated bandits suffer super-linear theoretical regret upper bound
in high-dimensional scenarios and are at risk of client information leakage due to their in-
ability to separate exploration from exploitation. This paper proposes a class of bandit
policy design, termed Fedego Lasso, to complete the task of federated high-dimensional
online decision-making with sub-linear theoretical regret and local client privacy argument.
Fedego Lasso relies on a novel multi-client teamwork-selfish bandit policy design to per-
form decentralized collaborative exploration and federated egocentric exploration with log-
arithmic communication costs. Experiments demonstrate the effectiveness of the proposed
algorithms on both synthetic and real-world datasets.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Yu-Xiang_Wang1

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Submission Number: 761