Open Access

ARTICLE

Beyond Wi-Fi 7: Enhanced Decentralized Wireless Local Area Networks with Federated Reinforcement Learning

Rashid Ali^1,*, Alaa Omran Almagrabi^2,3

1 Department of Engineering Science, University West, Trollhattan, 46132, Sweden
2 Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
3 Future Networks and Cyber-Physical Systems (FuN-CPS)-Research Group, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

* Corresponding Author: Rashid Ali. Email:

Computers, Materials & Continua 2026, 86(3), 12 https://doi.org/10.32604/cmc.2025.070224

Received 10 July 2025; Accepted 12 November 2025; Issue published 12 January 2026

Abstract

Wi-Fi technology has evolved significantly since its introduction in 1997, advancing to Wi-Fi 6 as the latest standard, with Wi-Fi 7 currently under development. Despite these advancements, integrating machine learning into Wi-Fi networks remains challenging, especially in decentralized environments with multiple access points (mAPs). This paper is a short review that summarizes the potential applications of federated reinforcement learning (FRL) across eight key areas of Wi-Fi functionality, including channel access, link adaptation, beamforming, multi-user transmissions, channel bonding, multi-link operation, spatial reuse, and multi-basic servic set (multi-BSS) coordination. FRL is highlighted as a promising framework for enabling decentralized training and decision-making while preserving data privacy. To illustrate its role in practice, we present a case study on link activation in a multi-link operation (MLO) environment with multiple APs. Through theoretical discussion and simulation results, the study demonstrates how FRL can improve performance and reliability, paving the way for more adaptive and collaborative Wi-Fi networks in the era of Wi-Fi 7 and beyond.

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