  Next-generation Wi-Fi networks are looking forward to introducing new
features like multi-link operation (MLO) to both achieve higher throughput and
lower latency. However, given the limited number of available channels, the use
of multiple links by a group of contending Basic Service Sets (BSSs) can result
in higher interference and channel contention, thus potentially leading to
lower performance and reliability. In such a situation, it could be better for
all contending BSSs to use less links if that contributes to reduce channel
access contention. Recently, reinforcement learning (RL) has proven its
potential for optimizing resource allocation in wireless networks. However, the
independent operation of each wireless network makes difficult -- if not almost
impossible -- for each individual network to learn a good configuration. To
solve this issue, in this paper, we propose the use of a Federated
Reinforcement Learning (FRL) framework, i.e., a collaborative machine learning
approach to train models across multiple distributed agents without exchanging
data, to collaboratively learn the the best MLO-Link Allocation (LA) strategy
by a group of neighboring BSSs. The simulation results show that the FRL-based
decentralized MLO-LA strategy achieves a better throughput fairness, and so a
higher reliability -- because it allows the different BSSs to find a link
allocation strategy which maximizes the minimum achieved data rate -- compared
to fixed, random and RL-based MLO-LA schemes.

Networking and Internet Architecture;Machine Learning