Research advanced in the integration of federated learning and reinforcement learning

Abstract

Reinforcement learning (RL) and federated learning (FL) are two important machine learning paradigms. Reinforcement learning is concerned with enabling intelligence to learn optimal policies when interacting with an environment, while federated learning is concerned with collaboratively training models on distributed equipment while preserving data privacy. In recent years, the fusion and complementarity of reinforcement learning, and federated learning have attracted increasing research interest, providing new directions for the development of the machine learning community. Focusing on the integration of reinforcement learning and federated learning, this paper introduces in detail the latest technological developments in the integration of reinforcement learning and federated learning, and discusses the main challenges, existing methods and future directions of this intersection. Specifically, based on the introduction of classical reinforcement learning and federated learning. In addition, this document introduces cutting-edge results on the integration of reinforcement learning and joint learning and discusses the problems and future directions of the integration.