Chen, Zhen; Zhang, Xiu Yin; So, Daniel KC; Wong, Kai-Kit; Chae, Chan-Byoung; Wang, Jiangzhou; (2024) Federated Learning Driven Sparse Code Multiple Access in V2X Communications. IEEE Network p. 1. 10.1109/MNET.2024.3375935. (In press).

Preview

Text FVC-2.pdf - Accepted Version Download (4MB) | Preview

Abstract

Sparse code multiple access (SCMA) is one of the competitive non-orthogonal multiple access techniques for the next generation multiple access systems. One of the main challenges is high computational complexity and the SCMA-aided codewords, that is, each terminal device maintains its local data and codewords, which provides no incentive for model updating to accommodate rapidly changing vehicle communication environment. Federated learning (FL) proves its effectiveness by enabling terminals to collaboratively train their local neural network models with private data while protecting the individual SCMA-aided codewords. To select reliable and trusted codewords, this article provides an overview of the salient characteristics of the application of federated learning-driven SCMA for vehicular communication and discusses its fundamental research challenges. Furthermore, we outline the advancement of federated learning-driven SCMA schemes and present a general framework with potential solutions to the challenges. Finally, several future research directions and open issues are discussed regarding federated learning-driven SCMA schemes.

Download activity - last month

Export as CSVJSONXML

Download activity - last 12 months

Export as XMLJSONCSV

Downloads by country - last 12 months

Export as CSVJSONXML

Archive Staff Only

View Item