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Federated learning for 6G communications: Challenges, methods, and future directions
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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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“FL can assist to improve the security and privacy of medical industry data analysis and prediction by incorporating a data protection system [25, 161, 162] .”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [162]
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“users, since only the gradient vectors calculated on the local data instead of the local data of the user are required to be uploaded to the BS [25].”
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“growing research interest owing to its salient advantages, such as accommodation of massive nodes, wide-range distribution jamming, reliability and robustness of recognition performance [7]-[9].”
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Federated Feature Selection for Horizontal Federated Learning in IoT Networks
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“To solve the data isolation problem, federated learning (FL) [13], [14], [15], [16], [17], [18], [19] becomes a promising research area.”
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Joint Training and Resource Allocation Optimization for Federated Learning in UAV Swarm
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“Thus, raw data with sensitive information does not need to be uploaded [11]–[14].”
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A Model Value Transfer Incentive Mechanism for Federated Learning With Smart Contracts in AIoT
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Metadata
- 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
- Published
- 2020-09
- Volume
- 17
- Issue
- 9
- Pages
- 105-118
- ISSN
- 1673-5447 (ISSN-L)
- 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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