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.
“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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“Machine learning aims to build models that can improve the future performance of a target through learning from the past experiences [26], [27], [28], [29], [30].”
BackgroundSelf-citationPrivacy and Security Concerns in Generative AI: A Comprehensive Survey
Golda, A., Mekonen, K. A., Pandey, A., Singh, A., Hassija, V., Chamola, V., & Sikdar, B. (2024) · IEEE Access · doi:10.1109/access.2024.3381611 · Cited by 269 · see citing works
“− Federated Learning [103]: updates models locally on user devices, keeps raw data on devices, reducing the risks associated with centralized data storage.”
BackgroundAdaptive Edge Association for Wireless Digital Twin Networks in 6G
Lu, Y., Maharjan, S., & Zhang, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3098508 · Cited by 265 · see citing works
“While 5G is still in the process of wide scale deployment, we observe significant work [13]–[15] on 6G vision and huge research interest in solving technological challenges that can enable the 6G paradigm.”
BackgroundSecurity Requirements and Challenges of 6G Technologies and Applications
AbdelHakeem, S. A., Hussein, H. H., & Kim, H. (2022) · Sensors · doi:10.3390/s22051969 · Cited by 247 · see citing works
“Thus, suspicious activity by malicious nodes must be foreseen during secure radio communication procedures [101].”
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“The ground-breaking advancements of 6G wireless networks [2]–[5] will dramatically improve the development of artificial intelligence (AI)- powered maritime IoT services.”
BackgroundFederated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework With UAV Swarms
Liu, Y., Nie, J., Li, X., Ahmed, S. H., Lim, W. Y. B., & Miao, C. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3021006 · Cited by 227 · see citing works
Self-citationFederated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review
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6G-Enabled Edge AI for Metaverse: Challenges, Methods, and Future Research Directions
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Self-citationA Survey of Blockchain and Artificial Intelligence for 6G Wireless Communications
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“[31] 2020 No Yes No No No ML, FL Lin et al.”
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“A major challenge in FL is the large communication and energy overhead related to exchanging the models updates between different end-users, and the centralized server [97], [98].”
BackgroundKang, J., Du, H., Li, Z., Xiong, Z., Ma, S., Niyato, D. T., & Li, Y. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3221990 · Cited by 188 · see citing works
“THE fast-growing 6G communication technology is enabling the transition from serving people and things to supporting the “Internet of Everything” [1], [2], [3].”
BackgroundSelf-citationExplainable AI Over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions
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“is widely used in 6G services [174], [175].”
Handling privacy-sensitive medical data with federated learning: Challenges and future directions
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“[22] introduce the application of FL to 6 G”
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Vision and research directions of 6G technologies and applications
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Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computation
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Federated Learning Over Wireless IoT Networks With Optimized Communication and Resources
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“Since the proposal of FL [5], there has been an increasing number of studies related to the implementation of FL over wireless networks [7]–[11].”
BackgroundFederated Learning for IoT: A Survey of Techniques, Challenges, and Applications
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Machine Learning in Real-Time Internet of Things (IoT) Systems: A Survey
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A vision on the artificial intelligence for 6G communication
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UAV Communications for Sustainable Federated Learning
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“Thanks to its characteristics, FL has been adopted in many applications such as Google keyboard suggestions [2], medicine [3], and mobile communications [4].”
Vehicular intelligence in 6G: Networking, communications, and computing
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Deep Federated Q-Learning-Based Network Slicing for Industrial IoT
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“Within each environment iteration, agents train local models using their own data, and then, upload local models to a logically centralized server that synthesizes a global model instead of raw data [23].”
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“end application reaches a desired level of accuracy, and the end devices (mobile nodes) also become more accurate [4].”
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PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks
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“The need for personalized federated learning was then proposed [9]–[12], where the needs and data distribution of individual clients are typically quite different.”
BackgroundLee, J., Solat, F., Kim, T. Y., & Poor, H. V. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3352910 · Cited by 111 · see citing works
“Other studies of [2], [3], [4], [5], [9] investigated the use of FL for 6G applications.”
BackgroundLow-Latency Federated Learning Over Wireless Channels With Differential Privacy
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“We can observe that our model is consistent with the asynchronous FL described in [34].”
ResultIoMT: A COVID-19 Healthcare System Driven by Federated Learning and Blockchain
Samuel, O., Omojo, A. B., Onuja, A. M., Sunday, Y., Tiwari, P., Gupta, D., Hafeez, G., Yahaya, A. S., Fatoba, O. J., & Shamshirband, S. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3143576 · Cited by 113 · see citing works
“Health institutions can now deploy AI applications to perform triad: treatment, prognoses and diagnosis; thereby, minimizing the waiting time for patients to consult medical doctors [23].”
BackgroundReputation-Based Federated Learning for Secure Wireless Networks
Song, Z., Sun, H., Yang, H. H., Wang, X., Zhang, Y., & Quek, T. Q. S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3079104 · Cited by 107 · see citing works
Self-citation6G vision: An ultra-flexible perspective
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“Especially distributed intelligence (edge AI) with edge computing is a promising paradigm for 6G communications [36].”
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“Diverse information in the sensing data can be used for in-depth analysis and knowledge extraction for optimal decision-making by using advanced machine learning technologies [1].”
BackgroundSelf-citationDecentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review
Witt, L., Heyer, M., Toyoda, K., Samek, W., & Li, D. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3231363 · Cited by 101 · see citing works
“In terms of communication rates, new developments are also expected once the 6G technology is introduced, which is predicted to be mutually empowering with FL [82].”
BackgroundToward 6G: An Overview of the Next Generation of Intelligent Network Connectivity
Tera, S. P., Chinthaginjala, R., Pau, G., & Kim, T. H. (2024) · IEEE Access · doi:10.1109/access.2024.3523327 · Cited by 105 · see citing works
6G: The Intelligent Network of Everything
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“antee ubiquitous and secure ML for the 6G networks [88].”
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“However, the traditional centralized machine learning techniques require global data, which is becoming a bottleneck of large-scale implementation due to the transmission overhead and significantly increased privacy concerns of raw training data exchange [10].”
BackgroundDai, L., & Wei, X. (2022) · IEEE Transactions on Communications · doi:10.1109/tcomm.2022.3175175 · Cited by 93 · see citing works
“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].”
BackgroundFederated learning for 6G-enabled secure communication systems: a comprehensive survey
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“Efficient training can also be conducted via distillation which consists of training a small model that mimics the behavior of the original model; therefore, significantly reducing the amount of required training data [269].”
BackgroundMethodInfluentialSplit Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions
Hafi, H., Brik, B., Frangoudis, P. A., Ksentini, A., & Bagaa, M. (2024) · IEEE Access · doi:10.1109/access.2024.3351600 · Cited by 90 · see citing works
“Most of them are focused on federated learning (FL) [9], [15], [33].”
InfluentialDistributed Few-Shot Learning for Intelligent Recognition of Communication Jamming
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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].”
BackgroundHasan, M. K., Habib, A. K. M. A., Islam, S., Safie, N., Ghazal, T. M., Khan, M. A., Alzahrani, A. I., Alalwan, N., Kadry, S., & Masood, A. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.02.044 · Cited by 93 · see citing works
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.”
BackgroundSecurity of federated learning with IoT systems: Issues, limitations, challenges, and solutions
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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].”
BackgroundA Novel Federated Learning Scheme for Generative Adversarial Networks
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Machine Learning for 6G Enhanced Ultra-Reliable and Low-Latency Services
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Optimizing Task Assignment for Reliable Blockchain-Empowered Federated Edge Learning
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“, security protection and task assignment problems, thus becoming the major roadblocks to their practical deployment [13], [19], [20].”
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“Some are as, Liu et al. [104] presented a detailed survey on FL with 6G communication along with VOLUME 10, 2022 TABLE 9.”
BackgroundSecuring massive IoT in 6G: Recent solutions, architectures, future directions
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Federated Learning in Robotic and Autonomous Systems
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“All this with zero raw data transmission [5].”
Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges
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“The application of AI in 6G enables its architecture to perform adaptive learning without manual intervention, thereby effectively supporting various Internet of Intelligence services [62].”
MethodSelf-citationTowards efficient communications in federated learning: A contemporary survey
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Self-citationMachine Learning Threatens 5G Security
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“Future research challenges [140] in the 5G scope include heterogeneity of systems as well as lack of cooper-”
Background6G and Internet of Things: a survey
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A survey on federated learning in data mining
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“…attempts to address the problem of communication overheads by model compression or reducing the amount of data sent back to the central server by the client (Hsieh et al., 2017; P. P. Liang et al., 2020; Luping et al., 2019; Malekijoo et al., 2021) or by LAN-WAN orchestration (Yuan et al., 2020).”
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A Model Value Transfer Incentive Mechanism for Federated Learning With Smart Contracts in AIoT
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Self-citationA Review of Indoor Positioning Systems for UAV Localization with Machine Learning Algorithms
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“…of the latest approaches, like Artificial Intelligence (AI), enabled micro base stations [13], machine learning [14], unmanned aerial vehicles (UAV) [15], blockchain [16], and human-centric communication [17] are potential concepts that can be used to design efficient net generation networks [18,19].”
BackgroundA Survey on Scheduling Techniques in Computing and Network Convergence
Tang, S., Yu, Y., Wang, H., Wang, G., Chen, W., Xu, Z., Guo, S., & Gao, W. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3329027 · Cited by 68 · see citing works
“Furthermore, sometimes the model size is too large for devices to respond in real-time, such as federated learning models, the schedulers could adopt weight tuning and sharing in such cases [278].”
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“Lastly, FL can enable the development and deployment of AI solutions for 6G networks by facilitating collaborative learning and inference among multiple devices [82,87].”
Background6G Communication Networks: Introduction, Vision, Challenges, and Future Directions
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“2, the network on the ground in 6G is the improved version of the present network [38, 40].”
BackgroundGao, B., Yang, F., Cui, N., Xiong, K., Lu, Y., & Wang, Y. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3214211 · Cited by 63 · see citing works
“Federated learning (FL) is an emerging paradigm that can enable contributing devices to collaboratively train a DL model without sharing their private data [8], [9], [10], [11], [12], [13], so it perfectly meets the needs of fingerprinting-based localization.”
BackgroundRobust Semisupervised Federated Learning for Images Automatic Recognition in Internet of Drones
Zhang, Z., Ma, S., Yang, Z., Xiong, Z., Kang, J., Wu, Y., Zhang, K., & Niyato, D. T. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3151945 · Cited by 61 · see citing works
“However, it generally requires a large amount of data with ground-truth labels to train a high-quality recognition model [6].”
BackgroundSelf-citationRetracted: A DQN-Based Frame Aggregation and Task Offloading Approach for Edge-Enabled IoMT
Yuan, X., Zhang, Z., Feng, C., Cui, Y., Garg, S., Kaddoum, G., & Yu, K. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3218313 · Cited by 57 · see citing works
“In order to meet the delay requirement of its QoS, we choose to send the data in Que[0] directly instead of aggregation.”
BackgroundInfluentialSelf-citationRetractedBlockchain technology meets 6 G wireless networks: A systematic survey
Hasan, K. M. B., Sajid, M., Lapina, M., Shahid, M., & Kotecha, K. V. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.02.031 · Cited by 62 · see citing works
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“The goal is to jointly train a common model, so that the participating devices train the local models and share only the updates, not the raw data, with the central parameter server [46].”
Nguyen, H. T., Nguyễn, M. T., Do, H. T., Hua, H. T., & Nguyen, C. V. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/5051328 · Cited by 56 · see citing works
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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