Digital Communications and Networks (Jun 2023)
Intelligent reflecting surface-assisted federated learning in multi-platoon collaborative networks
Abstract
Inspired by mobile edge computing (MEC), edge learning has gained a momentum by directly performing model training at network edge without sending massive data to a centralized data center. However, the quality of model training will be affected by the limited communication and computing resources of network edge. In this paper, how to improve the training performance of a federated learning system aided by intelligent reflecting surface (IRS) over vehicle platooning networks is studied, where multiple platoons train a shared federated learning model. Multi-platoon cooperation can alleviate the pressure of data processing caused by the limited computing resources of single platoon. Meanwhile, IRS can enhance the inter-platoon communication in a cost-effective and energy-efficient manner. Firstly, the federated learning optimization problem of maximizing the learning accuracy is formulated by jointing platoon scheduling, bandwidth allocation and phase shifts at the IRS to maximize the number of scheduled platoon. Specifically, in the proposed learning architecture each platoon updates the learning model with its own data and uploads it to the global model through IRS-based wireless networks. Then, a method based on sequential optimization algorithm (SOA) and a group-based optimization method are analyzed for single IRS aided and large-scale IRS aided communication, respectively. Finally, a platoon scheduling scheme is designed based on the communication reliability and computing reliability of platoons. Simulation results demonstrate that large-scale IRS assisted communication can effectively improve the reliability of multi-user communication networks. The scheduling scheme based on learning reliability balances the communication performance and computing performance of platoons.
