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Federated learning for 6G communications: Challenges, methods, and future directions
Open accessAbstract
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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Cites
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“computation and communication during artificial intelligence learning [3], which is able to support the diversified 6G services in an energy-efficient way [4], [5].”
BackgroundToward Federated Learning Models Resistant to Adversarial Attacks
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BackgroundAdaptive Upgrade of Client Resources for Improving the Quality of Federated Learning Model
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Self-citationWu, Y., Song, Y., Wang, T., Dai, M., & Quek, T. Q. S. (2022) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2022.3164574 · Cited by 25
Affordable federated edge learning framework via efficient Shapley value estimation
Dong, L., Liu, Z., Zhang, K., Yassine, A., & Hossain, M. S. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.05.007 · Cited by 25
Privacy protection against attack scenario of federated learning using internet of things
Yadav, K., Kariri, E., Alotaibi, S. D., Viriyasitavat, W., Dhiman, G., & Kaur, A. (2022) · Enterprise Information Systems · doi:10.1080/17517575.2022.2101025 · Cited by 23
“Reference (Liu et al. 2020b) introduced a malicious attacker to steal the original data through partially updated echelon information.”
BackgroundAdvancing Pandemic Preparedness in Healthcare 5.0: A Survey of Federated Learning Applications
Alsamhi, S. H., Hawbani, A., Shvetsov, A. V., & Kumar, S. (2023) · Advances in Human-Computer Interaction · doi:10.1155/2023/9992393 · Cited by 25
Energy-Efficient Massive MIMO for Federated Learning: Transmission Designs and Resource Allocations
Vu, T. T., Ngo, H. Q., Dao, M. N., Ngo, D. T., Larsson, E. G., & Le‐Ngoc, T. (2022) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2022.3222749 · Cited by 23
“Federated learning (FL) is a distributed learning framework that offers high privacy and communication efficiency [11]– [14].”
BackgroundDeep Reinforcement Learning Algorithm for Latency-Oriented IIoT Resource Orchestration
Zhang, P., Zhang, Y., Kumar, N., et al. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3229270 · Cited by 22
“T HE COMPLETION of the formulation of the 5G network standard also means that the research on the 6G network architecture needs to be further deepened to promote the development of the Industrial Internet of Things (IIoT) [1], [2].”
BackgroundOn the Dependability of 6G Networks
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Integrated Communication, Sensing, and Computation Framework for 6G Networks
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Self-citationA Review on Machine Learning for Channel Coding
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“Articles in [35], [36], [37], [38], and [39] reviewed the techniques of AI in wireless communication without focusing on the implementation of the channel coding compared to the articles in [40], [41], [42], [43], and [44].”
BackgroundDigital forensics challenges and readiness for 6G Internet of Things (IoT) networks
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Humans and robots: Friends of the future? A bird's eye view of biomanufacturing industry 5.0
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“A learning model is trained from the decentralized datasets at a centralized global server location [28].”
MethodLoss-Privacy Tradeoff in Federated Edge Learning
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Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
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Transforming Vehicular Networks: How 6G can Revolutionize Intelligent Transportation?
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“AI and machine learning technologies are expected to play a significant role in 6G. Studies [27, 40, 41] emphasize the intelligence of future 6G networks and explore the possibilities of applying machine learning technologies in 6G.”
BackgroundAn Efficient 6G Federated Learning-Enabled Energy-Efficient Scheme for UAV Deployment
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“The general population is reluctant to upload their personal data to other servers [14], [15].”
Understanding global aggregation and optimization of federated learning
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Nothing Wasted: Full Contribution Enforcement in Federated Edge Learning
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“the generated data in FEL, but their data can still contribute to training the shared ML model by iterative local learning, global aggregating, and updating [5].”
BackgroundSelf-citationSemi-Supervised Federated Learning with non-IID Data: Algorithm and System Design
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Self-citationKumar, A., Perveen, S., Singh, S., Kumar, A., Majhi, S., & Das, S. K. (2021) · International Conference on Computing, Communication and Security (ICCCS) · doi:10.1109/icccs51487.2021.9776334 · Cited by 18
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BackgroundA review of advances in 3D and 4D bioprinting: toward mass individualization paradigm
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“For instance, overseeing tasks involving programming industrial robots or managing translations demands a high level of technical proficiency (Liu et al., 2020).”
Distributed Learning in Intelligent Transportation Systems: A Survey
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Resource Allocation of NOMA Communication Systems for Federated Learning
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“Wireless federated learning (WFL) is a novel concept for distributed machine learning that helps wireless stations (WSs) to collaboratively build a shared learning model while locally preserving their own training data [2].”
BackgroundFederated Learning With Lossy Distributed Source Coding: Analysis and Optimization
Yang, H.-Y., Ding, T., & Yuan, X.-J. (2022) · IEEE Transactions on Communications · doi:10.1109/tcomm.2023.3277882 · Cited by 20
“Currently, the uplink communication cost appears to be a critical bottleneck in the employment of FL systems, especially for FL over wireless networks [5].”
BackgroundSelf-citationExploring SVM for Federated Machine Learning Applications
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Cooperative and smart attacks detection systems in 6G-enabled Internet of Things
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BackgroundAn Incentive Mechanism for Federated Learning: A Continuous Zero-Determinant Strategy Approach
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Self-citationLightweight privacy-preserving predictive maintenance in 6G enabled IIoT
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Accelerating Hybrid Federated Learning Convergence Under Partial Participation
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“The integration of federated learning (FL) with millimeter-Wave (mmWave) technology in cellular networks, spanning 4G, 5G, and emerging 6G systems, presents signi fi cant security and privacy challenges ( Liu et al., 2020 ).”
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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