Yang, Z;
Chen, M;
Wong, K-K;
Poor, HV;
Cui, S;
(2022)
Federated Learning for 6G: Applications, Challenges, and Opportunities.
Engineering
, 8
pp. 33-41.
10.1016/j.eng.2021.12.002.
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Abstract
Standard machine-learning approaches involve the centralization of training data in a data center, where centralized machine-learning algorithms can be applied for data analysis and inference. However, due to privacy restrictions and limited communication resources in wireless networks, it is often undesirable or impractical for the devices to transmit data to parameter sever. One approach to mitigate these problems is federated learning (FL), which enables the devices to train a common machine learning model without data sharing and transmission. This paper provides a comprehensive overview of FL applications for envisioned sixth generation (6G) wireless networks. In particular, the essential requirements for applying FL to wireless communications are first described. Then potential FL applications in wireless communications are detailed. The main problems and challenges associated with such applications are discussed. Finally, a comprehensive FL implementation for wireless communications is described.
| Type: | Article |
|---|---|
| Title: | Federated Learning for 6G: Applications, Challenges, and Opportunities |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.eng.2021.12.002 |
| Publisher version: | https://doi.org/10.1016/j.eng.2021.12.002 |
| Language: | English |
| Additional information: | Copyright 2022 The authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
| Keywords: | Federated learning; 6G; Reconfigurable intelligent surface; Semantic communication; Sensing; communication and computing |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10129939 |
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