Cited by 466Privacy-Preserving Technologies in DataAdvanced Wireless Communication TechnologiesSoftware-Defined Networks and 5G

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Federated learning for 6G communications: Challenges, methods, and future directions

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Liu, Yi, Yuan, Xingliang, Xiong, Zehui, Kang, Jiawen, Wang, Xiaofei, Niyato, Dusit·2020·China Communications

DOI 10.23919/jcc.2020.09.009

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Abstract

As the 5G communication networks are being widely deployed worldwide, both industry and academia have started to move beyond 5G and explore 6G communications. It is generally believed that 6G will be established on ubiquitous Artificial Intelligence (AI) to achieve data-driven Machine Learning (ML) solutions in heterogeneous and massive-scale networks. However, traditional ML techniques require centralized data collection and processing by a central server, which is becoming a bottleneck of large-scale implementation in daily life due to significantly increasing privacy concerns. Federated learning, as an emerging distributed AI approach with privacy preservation nature, is particularly attractive for various wireless applications, especially being treated as one of the vital solutions to achieve ubiquitous AI in 6G. In this article, we first introduce the integration of 6G and federated learning and provide potential federated learning applications for 6G. We then describe key technical challenges, the corresponding federated learning methods, and open problems for future research on federated learning in the context of 6G communications.

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Authors: Liu, Y., Yuan, X., Xiong, Z., Kang, J., Wang, X., & Niyato, D.
Affiliations: Heilongjiang University (CN)
Monash University (AU)
Nanyang Technological University (SG)
Tianjin University (CN)
National University of Singapore (SG)
ORCID: Niyato, Dusit
Venue: China Communications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-09
Volume: 17
Issue: 9
Pages: 105-118
ISSN: 1673-5447 (ISSN-L)
DOI: 10.23919/jcc.2020.09.009
OpenAlex: W3033403733
Cited by: 464
References: 52
Citations by year: 2026: 40 · 2025: 48 · 2024: 80 · 2023: 100 · 2022: 114 · 2021: 71 · 2020: 11
FWCI: 29.07
Type: journal article
Language: en
Open access: green
arxiv.org
Locations: China Communications (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
Keywords: federated learning, 6G communication, privacy preservation, wireless networks, distributed learning, 5G

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Federated learning for 6G communications: Challenges, methods, and future directions

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