Collaborative Learning in Kernel-based Bandits for Distributed Users

• URL: http://arxiv.org/abs/2207.07948v2
• Date: Mon, 17 Apr 2023 15:45:37 GMT
• Title: Collaborative Learning in Kernel-based Bandits for Distributed Users
• Authors: Sudeep Salgia, Sattar Vakili, Qing Zhao
• Abstract summary: We study collaborative learning among distributed clients facilitated by a central server. Each client is interested in maximizing a personalized objective function that is a weighted sum of its local objective and a global objective. We adopt the kernel-based bandit framework where the objective functions belong to a reproducing kernel Hilbert space.
• Score: 16.0251555430107
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study collaborative learning among distributed clients facilitated by a central server. Each client is interested in maximizing a personalized objective function that is a weighted sum of its local objective and a global objective. Each client has direct access to random bandit feedback on its local objective, but only has a partial view of the global objective and relies on information exchange with other clients for collaborative learning. We adopt the kernel-based bandit framework where the objective functions belong to a reproducing kernel Hilbert space. We propose an algorithm based on surrogate Gaussian process (GP) models and establish its order-optimal regret performance (up to polylogarithmic factors). We also show that the sparse approximations of the GP models can be employed to reduce the communication overhead across clients.

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