Collaborative Learning in Kernel-based Bandits for Distributed Users
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.
Cite
@article{arxiv.2207.07948,
title = {Collaborative Learning in Kernel-based Bandits for Distributed Users},
author = {Sudeep Salgia and Sattar Vakili and Qing Zhao},
journal= {arXiv preprint arXiv:2207.07948},
year = {2023}
}