eprintid: 10190184
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/19/01/84
datestamp: 2024-04-08 07:47:22
lastmod: 2024-04-08 07:47:22
status_changed: 2024-04-08 07:47:22
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Chen, Zhen
creators_name: Zhang, Xiu Yin
creators_name: So, Daniel KC
creators_name: Wong, Kai-Kit
creators_name: Chae, Chan-Byoung
creators_name: Wang, Jiangzhou
title: Federated Learning Driven Sparse Code Multiple Access in V2X Communications
ispublished: inpress
divisions: UCL
divisions: B04
divisions: C05
divisions: F46
keywords: Federated learning, sparse code multiple access
(SCMA), reconfigurable intelligent surfaces, vehicular communication
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2024-03-19
date_type: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
official_url: http://dx.doi.org/10.1109/mnet.2024.3375935
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2263900
doi: 10.1109/MNET.2024.3375935
lyricists_name: Wong, Kai-Kit
lyricists_id: KWONG98
actors_name: Wong, Kai-Kit
actors_id: KWONG98
actors_role: owner
full_text_status: public
publication: IEEE Network
pagerange: 1-1
issn: 0890-8044
citation:        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 <https://doi.org/10.1109/MNET.2024.3375935>.    (In press).    Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10190184/1/FVC-2.pdf