Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with
different features about the same set of users jointly train machine learning models without …

Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of
Things (IoT) applications and services, spanning from recommendation systems and speech …

This paper presents a novel federated linear contextual bandits model, where individual
clients face different $ K $-armed stochastic bandits coupled through common global …

The growing literature of Federated Learning (FL) has recently inspired Federated
Reinforcement Learning (FRL) to encourage multiple agents to federatively build a better …

Graph machine learning has gained great attention in both academia and industry recently.
Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are …

A general framework of personalized federated multi-armed bandits (PF-MAB) is proposed,
which is a new bandit paradigm analogous to the federated learning (FL) framework in …

Federated multi-armed bandits (FMAB) is a new bandit paradigm that parallels the federated
learning (FL) framework in supervised learning. It is inspired by practical applications in …

We study federated contextual linear bandits, where $ M $ agents cooperate with each other
to solve a global contextual linear bandit problem with the help of a central server. We …

Bayesian optimization (BO) has recently been extended to the federated learning (FL)
setting by the federated Thompson sampling (FTS) algorithm, which has promising …

This paper studies federated linear contextual bandits under the notion of user-level
differential privacy (DP). We first introduce a unified federated bandits framework that can …