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 potential of VLC, namely, limited modulation bandwidth, ambient light interference, optical diffuse reflection effects, devices' nonlinearity, and random receiver orientation. On the contrary, centralized machine learning (ML) techniques have demonstrated significant potential in handling different challenges related to wireless communication systems. Specifically, it has been 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 led to 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.

中文翻译：

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迈向 6G 系统中支持联合学习的可见光通信

可见光通信 (VLC) 技术被引入作为下一代无线网络的关键推动力，这主要归功于其简单且低成本的实施方式。然而，一些挑战阻碍了 VLC 的全部潜力的实现，即有限的调制带宽、环境光干扰、光学漫反射效应、设备的非线性和随机接收器方向。相反，集中式机器学习 (ML) 技术在处理与无线通信系统相关的不同挑战方面显示出巨大的潜力。具体来说，已经表明 ML 算法在处理复杂的网络任务方面表现出卓越的能力，例如信道均衡、估计和建模、资源分配和机会性频谱访问控制等。然而，在与服务器共享相关客户端的原始数据时，与隐私和通信开销有关的问题构成了集中式 ML 技术实施的主要瓶颈。这导致了一种新的分布式 ML 范式的出现，即联邦学习 (FL)，它可以降低与传输原始数据相关的成本，并通过在客户端本地和协作地训练 ML 模型来保护隐私。因此，很明显，将 FL 集成到 VLC 网络中可以提供 VLC 系统的普遍且可靠的实现。有了这个动机，这是文献中关于 FL 在 VLC 网络中应用的第一篇深入评论。为此，除了 FL 的不同架构和相关特性外，我们对基于 FL 的 VLC 系统的主要设计方面进行了全面的概述。最后，我们还强调了 FL 的一些潜在的未来研究方向，这些方向旨在显着提高 VLC 系统的性能和鲁棒性。