In this paper, we study Federated Bandit, a decentralized Multi-Armed Bandit problem with a
set of N agents, who can only communicate their local data with neighbors described by a …

We consider a multi-armed bandit game where N players compete for K arms for T turns.
Each player has different expected rewards for the arms, and the instantaneous rewards are …

Mobile edge computing facilitates users to offload computation tasks to edge servers for
meeting their stringent delay requirements. Previous works mainly explore task offloading …

We provide two distributed confidence ball algorithms for solving linear bandit problems in
peer to peer networks with limited communication capabilities. For the first, we assume that …

We study a decentralized cooperative stochastic multi-armed bandit problem with K arms on
a network of N agents. In our model, the reward distribution of each arm is the same for each …

We study a multiplayer stochastic multi-armed bandit problem in which players cannot
communicate, and if two or more players pull the same arm, a collision occurs and the …

Despite the significant interests and many progresses in decentralized multi-player multi-
armed bandits (MP-MAB) problems in recent years, the regret gap to the natural centralized …

We consider a collaborative online learning paradigm, wherein a group of agents connected
through a social network are engaged in learning a stochastic multi-armed bandit problem …

Next generation networks are expected to be ultra-dense and aim to explore spectrum
sharing paradigm that allows users to communicate in licensed, shared as well as …

The decentralized stochastic multi-player multi-armed bandit (MP-MAB) problem, where the
collision information is not available to the players, is studied in this paper. Building on the …