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

This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL),
an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of …

One of the fundamental issues for Federated Learning (FL) is data heterogeneity, which
causes accuracy degradation, slow convergence, and the communication bottleneck issue …

Abstract We study Federated Reinforcement Learning (FedRL) problem in which $ n $
agents collaboratively learn a single policy without sharing the trajectories they collected …

Federated learning (FL) is an emerging practical framework for effective and scalable
machine learning among multiple participants, such as end users, organizations and …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

Recently, many cooperative distributed multiagent reinforcement learning (MARL)
algorithms have been proposed in the literature. In this work, we study the effect of …

Federated learning is promising in enabling large-scale machine learning by massive
mobile devices without exposing the raw data of users with strong privacy concerns. Existing …

In recent years, federated learning (FL) has become a very popular paradigm for training
distributed, large-scale, and privacy-preserving machine learning (ML) systems. In contrast …

Federated learning (FL) is a training technique that enables client devices to jointly learn a
shared model by aggregating locally computed models without exposing their raw data …