Federated Multiagent Deep Reinforcement Learning for Intelligent IoT Wireless Communications: Overview and Challenges

Federated Multi-Agent Deep Reinforcement Learning (FMADRL) 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 with a special focus on optimized wireless communications for Internet of Things (IoT) applications. Firstly, we categorize state-of-theart 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 FMADRL, it is numerically compared to its fully centralized and distributed independent DRL counterparts, through a Sub- 6GHz/mmWave band association optimization problem for IoT wireless communications. Finally, we identify and discuss the open research directions and challenges, in order to spur further interest in this promising area.