  Visible light communication (VLC) technology was introduced as a key enabler
for the next generation of wireless networks, mainly thanks to its simple and
low-cost implementation. However, several challenges prohibit the realization
of the full potentials of VLC, namely, limited modulation bandwidth, ambient
light interference, optical diffuse reflection effects, devices non-linearity,
and random receiver orientation. On the contrary, centralized machine learning
(ML) techniques have demonstrated a significant potential in handling different
challenges relating to wireless communication systems. Specifically, it was
shown that ML algorithms exhibit superior capabilities in handling complicated
network tasks, such as channel equalization, estimation and modeling, resources
allocation, and opportunistic spectrum access control, to name a few.
Nevertheless, concerns pertaining to privacy and communication overhead when
sharing raw data of the involved clients with a server constitute major
bottlenecks in the implementation of centralized ML techniques. This has
motivated the emergence of a new distributed ML paradigm, namely federated
learning (FL), which can reduce the cost associated with transferring raw data,
and preserve privacy by training ML models locally and collaboratively at the
clients' side. Hence, it becomes evident that integrating FL into VLC networks
can provide ubiquitous and reliable implementation of VLC systems. With this
motivation, this is the first in-depth review in the literature on the
application of FL in VLC networks. To that end, besides the different
architectures and related characteristics of FL, we provide a thorough overview
on the main design aspects of FL based VLC systems. Finally, we also highlight
some potential future research directions of FL that are envisioned to
substantially enhance the performance and robustness of VLC systems.
