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Towards Federated Learning-Enabled Visible Light Communication in 6G Systems
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  • Shimaa Naser ,
  • Lina Bariah ,
  • Sami Muhaidat ,
  • Mahmoud Al-Qutayri ,
  • Paschalis C. Sofotasios ,
  • Ernesto Damiani ,
  • Merouane Debbah
Shimaa Naser
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Lina Bariah
Khalifa University, Khalifa University, Khalifa University

Corresponding Author:[email protected]

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Sami Muhaidat
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Mahmoud Al-Qutayri
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Paschalis C. Sofotasios
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Ernesto Damiani
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Merouane Debbah
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Abstract

Visible light communication is envisaged as a promising enabling technology for sixth generation (6G) and beyond networks. It was introduced as a key enabler for reliable massive-scale connectivity, mainly thanks to its simple and low-cost implementation which require minor variations to the existing indoor lighting systems. The key features of VLC allow offloading data traffic from the current congested radio frequency (RF) spectrum in order to achieve effective short-range, high speed, and green communications. However, several challenges prevent the realization of the full potentials of VLC, namely the limited modulation bandwidth of light emitting diodes, the interference resulted from ambient light, the effects of optical diffuse reflection, the non-linearity of devices, and the random receiver orientation. Meanwhile, centralized machine learning (ML) techniques have exhibited great potentials in handling different challenges in communication systems. Specifically, it has been recently shown that ML algorithms exhibit superior capabilities in handling complicated network tasks, such as channel equalization, estimation and modeling, resources allocation, opportunistic spectrum access control, non-linearity compensation, performance monitoring, detection, decoding/encoding, and network optimization. Nevertheless, concerns relating to privacy and communication overhead when sharing raw data of the involved clients with a server constitute major bottlenecks in large-scale implementation of centralized ML techniques. This has motivated the emergence of a new distributed ML paradigm, namely federated learning (FL). This method can reduce the cost associated with transferring the raw data, and preserve clients privacy by training ML model locally and collaboratively at the clients side. Thus, the integration of FL in VLC networks can provide ubiquitous and reliable implementation of VLC systems. Based on this, for the first time in the open literature, we provide an overview about VLC technology and FL. Then, we introduce FL and its integration in VLC networks and provide an overview on the main design aspects. Finally, we highlight some interesting future research directions of FL that are envisioned to boost the performance of VLC systems.
Feb 2022Published in IEEE Wireless Communications volume 29 issue 1 on pages 48-56. 10.1109/MWC.005.00334