Communication-Efficient Federated Learning for Hybrid VLC/RF Indoor Systems

Abstract

Federated Learning (FL) enables smart devices to collaboratively train Machine Learning (ML) models in a distributed manner without sharing their private data with a central server. However, the disparity between the communication and computation capabilities, and the heterogeneity of local datasets of smart devices degrades the performance of FL in terms of latency and accuracy. To mitigate this effect, we address the problems of device selection and resource allocation in an indoor environment where multiple smart devices participate in the FL process. To further reduce the communication latency, we use Visible Light Communication (VLC) for the downlink transmission while a Radio Frequency (RF) access point supports the uplink transmission in the proposed system. Accordingly, we formulate a multi-objective optimization problem for joint device selection and resource allocation in a hybrid VLC/RF system. Then, using the weight methods, the problem is converted to a single-objective optimization which is solved by incrementally selecting devices in each iteration. The embedded device selection scheme in the proposed algorithm is based on the significance of candidate devices’ local gradients and their alignment with the global tendency in order to intelligently prioritize the candidates in the training procedure. Simulation results show that the joint device selection and resource allocation scheme improves the accuracy of the ML model and reduces the average delay in presence of both system and data heterogeneity. Additionally, the proposed hybrid VLC/RF system decreases the latency of the FL process in the downlink mode compared to conventional RF systems.