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