TY  - INPR
SP  - 1
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
KW  - Federated learning
KW  -  sparse code multiple access
(SCMA)
KW  -  reconfigurable intelligent surfaces
KW  -  vehicular communication
PB  - Institute of Electrical and Electronics Engineers (IEEE)
JF  - IEEE Network
A1  - Chen, Zhen
A1  - Zhang, Xiu Yin
A1  - So, Daniel KC
A1  - Wong, Kai-Kit
A1  - Chae, Chan-Byoung
A1  - Wang, Jiangzhou
AV  - public
TI  - Federated Learning Driven Sparse Code Multiple Access in V2X Communications
Y1  - 2024/03/19/
SN  - 0890-8044
ID  - discovery10190184
UR  - http://dx.doi.org/10.1109/mnet.2024.3375935
EP  - 1
N2  - 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.
ER  -