Federated learning (FL) has demonstrated great potential in revolutionizing distributed
machine learning, and tremendous efforts have been made to extend it beyond the original …

Most of the existing federated multi-armed bandits (FMAB) designs are based on the
presumption that clients will implement the specified design to collaborate with the server. In …

We study the federated pure exploration problem of multi-armed bandits and linear bandits,
where $ M $ agents cooperatively identify the best arm via communicating with the central …

We study best arm identification (BAI) in linear bandits in the fixed-budget regime under
differential privacy constraints, when the arm rewards are supported on the unit interval …

We study best arm identification in a variant of the multi-armed bandit problem where the
learner has limited precision in arm selection. The learner can only sample arms via certain …

In this paper, we address the problem of best arm identification (BAI) with arm erasures in a
multi-armed bandit setting with finitely many arms. A learner who seeks to identify the best …