Federated Combinatorial Multi-Agent Multi-Armed Bandits

arXiv:2405.05950v1 Announce Type: new
Abstract: This paper introduces a federated learning framework tailored for online combinatorial optimization with bandit feedback. In this setting, agents select subsets of arms, observe noisy rewards for these subsets without accessing individual arm information, and can cooperate and share information at specific intervals. Our framework transforms any offline resilient single-agent $(\alpha-\epsilon)$-approximation algorithm, having a complexity of $\tilde{\mathcal{O}}(\frac{\psi}{\epsilon^\beta})$, where the logarithm is omitted, for some function $\psi$ and constant $\beta$, into an online multi-agent algorithm with …

abstract agent agents arm arxiv cs.ai cs.dm cs.lg cs.ma federated learning feedback framework information multi-agent multi-armed bandits observe offline optimization paper resilient stat.ml subsets type