  As wireless standards evolve, more complex functionalities are introduced to
address the increasing requirements in terms of throughput, latency, security,
and efficiency. To unleash the potential of such new features, artificial
intelligence (AI) and machine learning (ML) are currently being exploited for
deriving models and protocols from data, rather than by hand-programming. In
this paper, we explore the feasibility of applying ML in next-generation
wireless local area networks (WLANs). More specifically, we focus on the IEEE
802.11ax spatial reuse (SR) problem and predict its performance through
federated learning (FL) models. The set of FL solutions overviewed in this work
is part of the 2021 International Telecommunication Union (ITU) AI for 5G
Challenge.
