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

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Version 2 2023-11-29, 19:29
Version 1 2023-08-28, 18:38
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posted on 2023-11-29, 19:29 authored by Hugo De OliveiraHugo De Oliveira, Megumi Kaneko, Lila Boukhatem

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.

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Email Address of Submitting Author

hugo.de-oliveira@universite-paris-saclay.fr

ORCID of Submitting Author

0000-0001-8678-4464

Submitting Author's Institution

Paris-Saclay University, France - National Institute of Informatics, Tokyo

Submitting Author's Country

  • France

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