Federated Multi-Agent Deep Reinforcement Learning for Intelligent IoT Wireless Communications

Abstract

<p>Federated Multi-Agent Deep Reinforcement Learning (F-MADRL) is gathering keen research interests, as it may offer efficient solutions towards meeting the extreme requirements of future wireless communication networks. By contrast to centralized Deep Reinforcement Learning (DRL) and Multi- Agent DRL (MADRL), F-MADRL enables edge devices to cooperate without sharing their private data, while reducing the delays and signaling costs inherent to centralized approaches. In this article, we explore the new opportunities brought by F-MADRL by conducting a holistic survey of its related recent works. Firstly, we categorize state-of-the-art F-MADRL approaches, based on some distinctive features such as aggregation frequency, signaling overhead and privacy level. To better illustrate the behavior and advantages of F-MADRL, it is numerically compared to its fully centralized and distributed independent DRL counterparts, through a Sub-6GHz/mmWave band association optimization problem for Internet of Things (IoT) wireless communications. Finally, we identify and discuss the open research directions and challenges, in order to spur further interest in this promising area.</p>