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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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Cites
Deep Learning with Differential Privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578 · cited 1×
“C. Privacy-preserving Federated Learning For 6G 1) Differentially Privacy : Differential privacy (DP) [32] techniques are proposed to protect the privacy of gradient information, thereby achieving cloud-level privacy protection.”
BackgroundFederated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210 · cited 8×
“Deploying FL models to devices generally involves model training and inference [11].”
BackgroundMethodInfluentialFederated Learning: Strategies for Improving Communication Efficiency
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766
6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies
Zhang, Z., Xiao, Y., Ma, Z., Xiao, M., Ding, Z., Lei, X., Karagiannidis, G. K., & Fan, P. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2921208 · Cited by 2,575 · cited 1×
“According to ITU-T’s 6G communication technology white paper, beyond and high-precision teleport technology will provide users with a variety of new services [1]–[4], [13].”
BackgroundTowards Federated Learning at Scale: System Design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan, H. B., et al. (2019) · arXiv · doi:10.48550/arxiv.1902.01046 · Cited by 2,334
Toward 6G Networks: Use Cases and Technologies
Giordani, M., Polese, M., Mezzavilla, M., Rangan, S., & Zorzi, M. (2020) · IEEE Communications Magazine · doi:10.1109/mcom.001.1900411 · Cited by 2,241 · cited 3×
“For example, industries such as remote surgery [14], cloud PLC [15], and intelligent transportation systems [16] will be empowered by the new service model to provide users with better services.”
BackgroundZhu, L., & Han, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-63076-8_2 · Cited by 1,906 · cited 3×
“3) Gradient Compression : The reason why adversaries can infer the local data of the devices is that the gradient information contains rich semantic information [27].”
BackgroundInfluentialDang, S., Amin, O., Shihada, B., & Alouini, M.-S. (2020) · Nature Electronics · doi:10.1038/s41928-019-0355-6 · Cited by 1,790 · cited 10×
“Key services and roadmap for 6G [1]–[4].”
BackgroundInfluentialThe Roadmap to 6G - AI Empowered Wireless Networks
Letaief, K., Chen, W., Shi, Y.-M., et al. (2019) · arXiv.org · doi:10.48550/arxiv.1904.11686 · Cited by 1,729 · cited 8×
“Key services and roadmap for 6G [1]–[4].”
BackgroundResultInfluentialA Joint Learning and Communications Framework for Federated Learning Over Wireless Networks
Chen, M., Yang, Z., Saad, W., Yin, C., Poor, H. V., & Cui, S. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2020.3024629 · Cited by 1,547
Flagged first, then by citations
3 of the 52 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- [19][19] M. Giordani, M. Polese, M. Mezzavilla, S. Rangan, and M. Zorzi, 'Toward 6g networks: Use cases and technologies,' IEEE Communications Magazine , vol. 58, no. 3, pp. 55-61, 2020. -
- [39][39] T.-D. Cao, T. Truong-Huu, H. Tran, and K. Tran, 'A federated learning framework for privacy-preserving and parallel training,' arXiv preprint arXiv:2001.09782 , 2020. - · arXiv
- [43][43] L. U. Khan, N. H. Tran, S. R. Pandey, W. Saad, Z. Han, M. N. Nguyen, and C. S. Hong, 'Federated learning for edge networks: Resource optimization and incentive mechanism,' arXiv preprint arXiv:1911.05642 , 2019. - · arXiv
Cited by
Sorted by citations
Items 201–300 of 466
Towards 6G Communications: Architecture, Challenges, and Future Directions
Mitra, P., Bhattacharjee, R., Chatterjee, T., De, S., Karmakar, R., Ghosh, A., & Adhikari, T. (2021) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt51525.2021.9580084 · Cited by 16
“We must keep preferable federated learning on the top priority list in machine learning applications, however they also suffer from fairness issues [18], [37].”
BackgroundKaur, J., & Khan, M. A. (2022) · Computer Networks · doi:10.1016/j.comnet.2022.108902 · Cited by 14
FedRelay: Federated Relay Learning for 6G Mobile Edge Intelligence
Li, P., Zhong, Y., Zhang, C., Wu, Y., & Yu, R. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3225087 · Cited by 18
“By integrating FL into 6G edge systems, data analytics can be carried out directly on the birthplace of data, which significantly democratizes artificial intelligence (AI) and improves the quality of service [5], [6], [7], [8].”
BackgroundAlahmari, S., & Alghamdi, I. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107849 · Cited by 21
Saputra, Y. M., Nguyen, D. N., Dinh, H. T., Pham, Q., Dutkiewicz, E., & Hwang, W. (2022) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2022.3178949 · Cited by 16
“The work in [12] investigates the FL with privacy awareness for 6 G including the use of differential privacy and trained model compression.”
BackgroundPartially Collaborative Edge Caching Based on Federated Deep Reinforcement Learning
Lei, M., Li, Q., Ge, X., & Pandharipande, A. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3206876 · Cited by 11
“When applied to collaborative caching among multiple agents, it incurs extra communication overhead and disclosure of users’ privacy [10], [11], [12], [13].”
BackgroundSecurity for IoT using Federated Learning
Bodagala, H., & Priyanka, H. (2022) · International Conference on Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC) · doi:10.1109/icmacc54824.2022.10093557 · Cited by 15
“[6] Difficulties and advantages of federated learning's use in smart city sensing was presented by Jiang et al [7].”
BackgroundEnergy-Efficient and Fair IoT Data Distribution in Decentralised Federated Learning
Zhao, J., Feng, Y., Chang, X., Xu, P. M., Li, S., Liu, C. H., Yu, W., Tang, J., & Crowcroft, J. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3185672 · Cited by 14
“The work in [15] proposes a hybrid optimisation algorithm to support the communication of IoT devices under 6G. AI will play a critical role in building more intelligent and automated 6G wireless cellular systems [16].”
MethodYan, S., Zhang, P., Huang, S., Wang, J., Sun, H., Zhang, Y., & Tolba, A. K. R. (2023) · Electronics · doi:10.3390/electronics12112478 · Cited by 14
“[25] considered that in the federated learning scenario based on edge computing, by splitting the model, some models are reserved for local training, and the rest of the models are unloaded to edge nodes for training, thus reducing the training task of end users but at the same time increasing the overhead of the comm…”
BackgroundA Comprehensive Review on Machine Learning-based Approaches for Next Generation Wireless Network
Paul, S. (2024) · SN Computer Science · doi:10.1007/s42979-024-02831-3 · Cited by 16
“…challenges in the systems of the edge of the network [176] FL FL Challenges, performance metrics, implementation challenges in 6G [177] Edge Computing, FL Edge computing, challenges, use cases, market survey [178] FL FL Methods, incorporation challenges, future trends in 6G Page 22 of 28”
A Survey of Federated Learning for mmWave Massive MIMO
Nugroho, V. A., & Lee, B. M. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3397302 · Cited by 16
“Liu et al. [7] covered challenges of FL for 6G communications.”
BackgroundArtificial Intelligence Algorithms in Flood Prediction: A General Overview
Pandey, M. K. (2024) · Springer eBooks · doi:10.1007/978-3-031-51053-3_12 · Cited by 15
Security and Trust in the 6G Era: Risks and Mitigations
Tripi, G., Iacobelli, A., Rinieri, L., & Prandini, M. (2024) · Electronics · doi:10.3390/electronics13112162 · Cited by 16
Challenges and opportunities of the transition from Industry 4.0 to Industry 5.0
Mourtzis, D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13924-6.00004-1 · Cited by 15
A D2D-Aided Federated Learning Scheme With Incentive Mechanism in 6G Networks
Fantacci, R., & Picano, B. (2022) · IEEE Access · doi:10.1109/access.2022.3232440 · Cited by 14
“The upcoming 6G networks will be empowered by artificial intelligence capabilities, in order to providing efficient and effective strategies to transmit, collect, merge, manipulate and interpret large amount of data, anytime and anywhere, to support disruptive applications and intelligent services, shifting the develo…”
BackgroundToward AI in 6G: Concepts, Techniques, and Standards
Shafaei, S., Palaios, A., Ennaceur, Z., Zhang, J., Pandit, V., Gautam, P., Gharouni, A., Gajic, B., Banerjee, B., Mallikarjun, S. B., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3595752 · Cited by 16
“With the increasing scale and complexity of 6G systems, training AI/ML models exclusively on a centralized server is often not feasible due to network bandwidth limitations and growing privacy concerns [177].”
Semi-supervised Federated Learning for Digital Twin 6G-enabled IIoT: A Bayesian estimated approach
Qi, Y., & Hossain, M. S. (2024) · Journal of Advanced Research · doi:10.1016/j.jare.2024.02.012 · Cited by 14
“With the widespread adoption of Industrial Internet of Things (IIoT) devices in various domains, including healthcare, transportation, and smart cities, the 6G network has become increasingly important for providing reliable and secure connectivity [26,32,39].”
Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks
Lin, Y., Wang, K., & Ding, Z. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3447833 · Cited by 15
“In light of the rapid growth of mobile applications and networked devices, traditional machine learning frameworks face new challenges in supporting the vast amount of data [3].”
BackgroundAdvancing Security in 5G Core Networks Through Unsupervised Federated Time Series Modeling
Sheikhi, S., & Kostakos, P. (2024) · IEEE International Conference on Cyber Security and Resilience (CSR) · doi:10.1109/csr61664.2024.10679491 · Cited by 13
Machine Learning for 5G and Beyond: Applications and Future Directions
Sarkar, S., & Debnath, A. (2021) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc51422.2021.9532728 · Cited by 12
“Liu et al.[18] 2020 • Federated learning algorithm”
MethodRumesh, Y., Senevirathna, T., Porambage, P., et al. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10278845 · Cited by 12
Federated Learning in Big Data with IoT for Intrusion Detection
Govindaram, A., Prasath, J. S., Suganya, A., Jayasakthi, K., Rajkumar, N., & Jose Anand, A. (2025) · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) · doi:10.1109/icmcsi64620.2025.10883562 · Cited by 13
Incentive mechanism for collaborative distributed learning in Artificial Intelligence of Things
Wang, G., Yin, J.-L., Hossain, M. S., & Muhammad, G. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.06.015 · Cited by 13
Rebirth of Distributed AI—A Review of eHealth Research
Khan, M. A., & Alkaabi, N. (2021) · Sensors · doi:10.3390/s21154999 · Cited by 13
“We also find surveys about the integration of the futuristic 6G network with FL, such as in [14], where the authors describe key technical challenges of such integration.”
BackgroundReview of Recent Technologies for Tackling COVID-19
Alharbi, A. A., & Rahman, M. A. (2021) · SN Computer Science · doi:10.1007/s42979-021-00841-z · Cited by 14
Deep learning-based privacy-preserving recommendations in federated learning
Kolli, C. S., Reddy, V. V. K., Reddy, T. S., Chandol, M. K., Dasari, D. B., & Reddy, M. R. K. (2024) · International Journal of General Systems · doi:10.1080/03081079.2024.2302605 · Cited by 11
“Conventional ML algorithms based on a centralized system suffer from security and privacy issues, and hence decentralized ML solutions are becoming a necessity (Liu et al. 2020).”
BackgroundAdvancing Federated Learning in 6G: A Trusted Architecture with Graph-Based Analysis
Ye, W., Qian, C., An, X., Yan, X., & Carle, G. (2023) · Globecom · doi:10.1109/globecom54140.2023.10436772 · Cited by 13
“Recent advancements in decentralized computing and communication capabilities of mobile networks have further facilitated the provision of FL services [3].”
BackgroundSelf-Evolving Integrated Vertical Heterogeneous Networks
Farajzadeh, A., Khoshkholgh, M. G., Yanıkömeroğlu, H., & Erçetin, Ö. (2023) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2023.3243870 · Cited by 18
“To start with, they each receive the current global model’s weights from the server, and train it on each of their local data to generate updated parameters which are then uploaded back to the server for aggregation [202], [203].”
Study of an Attenuator Supporting Meander-Line Slow Wave Structure for Ka-Band TWT
Wang, H., Wang, S., Wang, Z., et al. (2021) · Electronics · doi:10.3390/electronics10192372 · Cited by 11
Xie, X., Tang, H. J., Yang, H., Huang, Q., & Dong, P. (2021) · IEEE Systems Journal · doi:10.1109/jsyst.2021.3120299 · Cited by 11
Efficient Edge Intelligence under Clustering for UAV Swarm Networks
Qu, Y., Dong, C., Wu, T., et al. (2021) · doi:10.1109/sagc52752.2021.00026 · Cited by 11
URLLC Resource Slicing and Scheduling in 5G Vehicular Edge Computing
Hao, M., Ye, D., Wang, S., Tan, B., & Yu, R. (2021) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2021-spring51267.2021.9448805 · Cited by 11
“Ultra Reliable Low Latency Communications (URLLC) is one of the most important features of 5th generation (5G) technology at the commercialization stage [1] [2].”
BackgroundAneja, N., Aneja, S., & Bhargava, B. (2023) · Wireless Communications and Mobile Computing · doi:10.1155/2023/8658278 · Cited by 11
Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence.Preprint
Baccour, E., Mhaisen, N., Abdellatif, A. A., Erbad, A., Mohamed, A. M. S., Hamdi, M., & Guizani, M. (2021) · arXiv (Cornell University) · Cited by 11
Artificial intelligence for next-generation 6G technologies and networks
Tok, Y. E., Toprak, A. G., Karahan, S. N., et al. (2026) · Discover Networks · doi:10.1007/s44354-026-00016-3 · Cited by 10
“Considering the dense connectivity, heterogeneous data sources, low-latency, and high-level security requirements of 6G networks, FL is a very crucial approach for 6G communication [79].”
Leveraging decentralized communication for privacy-preserving federated learning in 6G Networks
Teixeira, R., Baldoni, G., Antunes, M., et al. (2025) · Computer Communications · doi:10.1016/j.comcom.2025.108072 · Cited by 10
Privacy-Preserving Federated Edge Learning: Modeling and Optimization
Liu, T., Di, B., & Song, L. (2022) · IEEE Communications Letters · doi:10.1109/lcomm.2022.3167088 · Cited by 10
Latency Minimization for Wireless Federated Learning With Heterogeneous Local Model Updates
Zhu, J.-Y., Shi, Y.-M., Fu, M., Zhou, Y., Wu, Y., & Fu, L. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3285937 · Cited by 11
“FL has received increasing attention in many critical applications, ranging from 6G communication [4], [5], [6], [7], [8], autonomous driving [9], IoT [10], blockchain [11], [12], to biomedical healthcare [13], [14], and drug discovery [15].”
BackgroundSemi-Asynchronous Federated Split Learning for Computing-Limited Devices in Wireless Networks
Ao, H., Tian, H., Ni, W., et al. (2025) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2025.3546448 · Cited by 9
Self-citationAdaptive Model Pruning in Decentralized Federated Learning
Mu, Y., Liu, X., & Ratnarajah, T. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3583472 · Cited by 10
A Personalized Privacy-Preserving Scheme for Federated Learning
Li, Z. (2022) · IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) · doi:10.1109/eebda53927.2022.9744805 · Cited by 11
“A variety of privacy-preserving schemes are used to guarantee a local-level privacy for clients’ sensitive data in FL[9].”
BackgroundApplication of Federated Learning in Telecommunications and Edge Computing
Mangla, U. (2022) · Springer eBooks · doi:10.1007/978-3-030-96896-0_25 · Cited by 11
Congruent Learning for Self-Regulated Federated Learning in 6G
Taghia, J., Moradi, F., Larsson, H., et al. (2024) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2023.3347680 · Cited by 9
“To avoid the high cost from overhead of data collection and raw data transmission while safeguarding private data, privacy-preserving distributed ML techniques such as federated learning are becoming more prevalent in the communication and networking domain [11], [12], [13], [14], [15].”
Federated Analytics With Data Augmentation in Domain Generalization Toward Future Networks
Zhang, X., Parra-Ullauri, J. M., Moazzeni, S., Vasilakos, X., Nejabati, R., & Simeonidou, D. E. (2024) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2024.3393892 · Cited by 9
“Recently, FL has been considered an essential part of the next generation (6G) of networks [30], [31].”
BackgroundMEC-Based Jamming-Aided Anti-Eavesdropping with Deep Reinforcement Learning for WBANs
Chen, G., Liu, X., Shorfuzzaman, M., Karime, A., Wang, Y., & Qi, Y. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3453186 · Cited by 9
“Nevertheless, as a particular type of Wireless Sensor Networks (WSNs), the openness and broadcast nature of the wireless transmission leave WBAN to confront eavesdropping attack with high probability [14, 16, 17, 23], which may reveal personal health information [15], the location of the user, and so on.”
BackgroundLightFIDS: Lightweight and Hierarchical Federated IDS for Massive IoT in 6G Network
Alotaibi, A. I., & Barnawi, A. (2023) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-023-08439-8 · Cited by 9
Jiang, W., Xiao, Z., Qian, L.-J., Qin, S., Feng, G., & Wu, Y. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3568875 · Cited by 9
Unveiling the future: A comprehensive analysis of 6G technology and its transformative potential
Jain, M., Verma, R., Kumar, S., Kumar, G., Chamola, V., & Guizani, M. (2025) · Telecommunication Systems · doi:10.1007/s11235-025-01323-w · Cited by 11
“• Adaptivelearningmechanismssuchasfederatedorincre-mental learning [186, 187], which allow models to evolve with minimal computational cost.”
Intrusion Detection Using Federated Learning for Computing
Aashmi, R. S., & Jaya, T. (2022) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.027216 · Cited by 8
A Review of Recent Developments in 6G Communications Systems
Kamath, S., Anand, S., Buchke, S., et al. (2024) · doi:10.3390/engproc2023059167 · Cited by 8
Leveraging User Equipment for Radio Access Network Augmentation
Pérez-Romero, J., & Sallent, O. (2021) · IEEE Conference on Standards for Communications and Networking · doi:10.1109/cscn53733.2021.9686119 · Cited by 8
Robust Multi-model Personalized Federated Learning via Model Distillation
Muhammad, A., Lin, K. B., Gao, J. Y., & Chen, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-95391-1_27 · Cited by 12
Worker-Centric Model Allocation for Federated Learning in Mobile Edge Computing
Huang, H., Yang, Y., Jiang, Z., & Zheng, Z. (2022) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2022.3187335 · Cited by 8
“The forthcoming 5G/6G technologies will essentially enhance edge-computing networks by the substantial improvement on throughput of wireless networks [26].”
BackgroundOver-the-Air Federated Averaging With Limited Power and Privacy Budgets
Yan, N., Wang, K., Pan, C.-H., Chai, K., Shu, F., & Wang, J.-Z. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2023.3338738 · Cited by 10
Federated Learning-based Power Control and Computing for Mobile Edge Computing System
Yang, T., Li, X., & Shao, H. (2021) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2021-fall52928.2021.9625545 · Cited by 8
AI-powered Infrastructures for Intelligence and Automation in Beyond-5G Systems
Militano, L., Zafeiropoulos, A., Fotopoulou, E., et al. (2021) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps52748.2021.9682117 · Cited by 7
“…contrast to traditional centralized ML techniques where all data samples are uploaded to one centralized server for model updates computation, FL trains a model across multiple decentralized devices holding local data samples (e.g. decentralized devices in various parts of a 5G network [12]- [13]).”
BackgroundGorla, P., Keerthivasan, V. S., Chamola, V., et al. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3208522 · Cited by 7
“Synchronous algorithms for aggregation avoid the additional noise introduced from asynchronous aggregation [14], [15].”
BackgroundFED6G: Federated Chameleon Learning for Network Slice Management in Beyond 5G Systems
Thantharate, A. (2022) · IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · doi:10.1109/iemcon56893.2022.9946488 · Cited by 7
“The authors in [6] [7] presented the integration of federated and 6G learning and provided possible federated 6G learning applications.”
BackgroundCao, H., Feng, W., He, J., et al. (2023) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2023.3307416 · Cited by 7
“Liu introduces the integration of 6G and federated learning and discusses the prospective applications and challenges of federated learning [6].”
BackgroundArtificial Intelligence Advancement for 6G Communication: A Visionary Approach
Miya, J., Raj, S., Ansari, M. A., Kumar, S. M., & Kumar, R. (2023) · Springer eBooks · doi:10.1007/978-3-031-30101-8_15 · Cited by 8
Federated Learning and Artificial Intelligence in E-Healthcare
Gupta, M., Sharma, P., & Kalra, R. (2023) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-1082-3.ch006 · Cited by 8
Towards dependable 6G networksPreprint
Ahmad, I., Rodriguez, F., Huusko, J., & Seppänen, K. (2022) · doi:10.36227/techrxiv.20237358 · Cited by 7
Zhang, X., & Yu, J. (2026) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2026.3660250 · Cited by 6
“The integration of D2RL with federated learning frameworks [49] presents another promising direction, potentially enabling privacy-preserving distributed training across multiple network domains.”
Multicore Federated Learning for Mobile-Edge Computing Platforms
Bai, Y., Chen, L., Li, J., Wu, J., Zhou, P., Xu, Z., & Xu, J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3224239 · Cited by 6
“A burst of research works have been done to improve the learning efficiency of FL over MEC systems, including wireless/computing resource allocation, convergence analysis, and client scheduling (see [6], [7], [8] and references therein).”
BackgroundBano, S., Tonellotto, N., Cassará, P., & Gotta, A. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.04.010 · Cited by 6
A FL-Based Radio Map Reconstruction Approach for UAV-Aided Wireless Networks
Tan, Z., Xiao, L., Tang, X., et al. (2023) · Electronics · doi:10.3390/electronics12132817 · Cited by 6
Federated Learning for 6G HetNets' Physical Layer Optimization
Bartsiokas, I. A., Gkonis, P. K., Papazafeiropoulos, A., Kaklamani, D. I., & Venieris, I. S. (2023) · Advances in information quality and management · doi:10.4018/978-1-6684-7366-5.ch070 · Cited by 6
Navigating the Ethical Landscape: Considerations in Implementing AI-ML Systems in Human Resources
Basnet, S. (2024) · International Journal of Research Publication and Reviews · doi:10.55248/gengpi.5.0324.0755 · Cited by 9
Basu, D., Jain, A., Rao, A. S., et al. (2022) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets53615.2022.9668409 · Cited by 6
SUDP: The Frontier Tool for Security in 5G and Beyond Wired or Wireless Communication
Rawal, B. S., & Shah, J. (2021) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps52748.2021.9681962 · Cited by 6
“To handle these problems the author [14] will suggest many different defensive methods like Robust aggregated algorithms.”
MethodA Joint Communication and Federated Learning Framework for Internet of Things Networks
Yang, Z., Jia, G., Chen, M., et al. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685679 · Cited by 6
Performance Analysis for Channel-Weighted Federated Learning in OMA Wireless Networks
Yan, N., Wang, K., Pan, C., & Chai, K. K. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3154653 · Cited by 6
“Federated learning (FL) [1] has been proposed as a distributed machine learning technique, where edge devices collaboratively train a model using only locally available data with the help of a parameter server (PS).”
Multi-frame Scheduling for Federated Learning over Energy-Efficient 6G Wireless Networks
Beitollahi, M., & Lu, N. (2022) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infocomwkshps54753.2022.9798090 · Cited by 6
A Performance Efficient Approach of Global Training in Federated Learning
Bhatti, D. M. S., & Nam, H. (2023) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic57133.2023.10066985 · Cited by 6
“Federated learning has already become a privacy-preserving approach of model training by deploying intelligence to the edge of network [2].”
FedCo: Self-Supervised Learning in Federated Learning with Momentum Contrast
Wei, S., Cao, G., Dai, C., Dai, S., & Guo, B. (2022) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys57074.2022.00192 · Cited by 6
“Whether supervised or unsupervised learning, the nonindependent and identically distributed(nonIID) problem of federation learning are still popular for research [14].”
BackgroundByzantine-Robust Multimodal Federated Learning Framework for Intelligent Connected Vehicle
Wu, N., Lin, X., Lu, J., Zhang, F., Chen, W., Tang, J., & Xiao, J. (2024) · Electronics · doi:10.3390/electronics13183635 · Cited by 11
Depression Detection Using Asynchronous Federated Optimization
Li, J., Zhang, R., Cen, M., et al. (2021) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom53373.2021.00110 · Cited by 6
Self-citationTactical Edge IoT in Defense and National Security
Fraga‐Lamas, P., & Fernández‐Caramés, T. M. (2022) · Wiley eBooks · doi:10.1002/9781119892199.ch20 · Cited by 7
Federated Large Language Models for Smart Grid: A Communication Efficient LoRA Approach
Deng, X., Ma, T., Li, H., & Lu, M. (2024) · IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · doi:10.1109/iccasit62299.2024.10827901 · Cited by 5
Li, D., Lv, R., Li, J., et al. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3557247 · Cited by 5
“As an important branch of the Internet of Things technology, WBANs offer a promising solution to this challenge [17].”
Using machine learning for security issues in cognitive IoT
Alzuabi, W., Elmedany, W. M., & Sharif, M. S. (2024) · IET conference proceedings. · doi:10.1049/icp.2024.0980 · Cited by 6
Huang, L., Shao, L., Bao, M., Guo, C., Shao, Z., Huang, X., Wang, M., Jiang, X., & Hu, S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109233 · Cited by 6
Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
Seol, J., Kim, H.-Y., Kancharla, A., & Kim, J. (2024) · Information · doi:10.3390/info15110727 · Cited by 8
A Survey on Enabling Technologies and Recent Advancements in 6G Communication
Kamath, H. S., Bhandari, A., Shekhar, S., et al. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2466/1/012005 · Cited by 5
“The ML model is shared between devices, and no data exchange is needed as only the training parameters of the model are shared[44].”
Farcas, A.-J., Lee, M., Kompella, R. R., Latapie, H. M., Veciana, G. A. D., & Mărculescu, R. (2023) · IEEE/ACM International Conference on Internet-of-Things Design and Implementation (IoTDI) · doi:10.1145/3576842.3582378 · Cited by 5
“In particular, the impact on communication is the most important one [16, 20], especially since the edge devices may struggle to communicate the trained model back and forth with the cloud.”
BackgroundDistributed Multi-Agent Empowered Resource Allocation in Deep Edge Networks
Gong, Y., Wang, J., & Yao, H. (2021) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc51323.2021.9498936 · Cited by 5
Privacy Preserving Federated RSRP Estimation for Future Mobile Networks
Haliloğlu, Ö., Soykan, E. Ü., & Alabbasi, A. (2021) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps52748.2021.9682084 · Cited by 6
“A comprehensive review of the application of FL over 6G network is described in [17] and [18].”
BackgroundRobust and Resilient Federated Learning for Securing Future Networks
Siriwardhana, Y., Porambage, P., Liyanage, M., & Ylianttila, M. (2022) · Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit) · doi:10.1109/eucnc/6gsummit54941.2022.9815812 · Cited by 5
A Robust Aggregation Approach for Heterogeneous Federated Learning
Bhatti, D. M. S., & Nam, H. (2023) · International Conference on Ubiquitous and Future Networks · doi:10.1109/icufn57995.2023.10201227 · Cited by 5
“By incorporating intelligence at the edge of the network, It has become an efficient model for privacy-preserving learning [2].”
BackgroundLow-Latency Federated Learning over Wireless Channels with Differential PrivacyPreprint
Wei, K., Li, J., Ma, C., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.13039 · Cited by 5
“We can observe that our model is consistent with the asynchronous FL described in [34].”
ResultFairness-Aware Multi-Server Federated Learning Task Delegation Over Wireless Networks
Gao, Y., Ren, C., Yu, H., Xiao, M., & Skoglund, M. (2024) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2024.3508594 · Cited by 4
“To address this limitation, we propose Fairness-Aware Multi-Server FL task delegation approach ( FAMuS ), a novel framework based on Contract Theory and Lyapunov optimization to jointly address these intricate issues facing wireless multi-server FL networks (WMSFLN).”
AbuAlghanam, O., Alazzam, H., Almobaideen, W. A., Saadeh, M., & Saadeh, H. (2025) · Sensors · doi:10.3390/s25082357 · Cited by 4
“This iterative process, including local training, model aggregation, and model updates, continues until the desired level of model performance is achieved or convergence is reached [15,27,28].”
Revolutionizing Machine Learning Security: The Role of Quantum-Enhanced Federated Learning
Kannan, E., Ravikumar, S., J, C. M. B. M., Vijay, K., Vathani, A., & Kannan, S. (2024) · International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs63125.2024.10895237 · Cited by 5
Xu, W., Zhang, D., Ding, Z., et al. (2021) · International Conference on Microwave and Millimeter Wave Technology (ICMMT) · doi:10.1109/icmmt52847.2021.9618180 · Cited by 4
Attacks Opportunities in the Cyber Physical Space and the Role of Cybersecurity
Aggarwal, N., Tripathi, B., Chottani, D., et al. (2021) · doi:10.1109/iccmst54943.2021.00064 · Cited by 4
A Communication Efficient Approach of Global Training in Federated Learning
Bhatti, D. M. S., Haris, M., & Nam, H. (2022) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc55196.2022.9952661 · Cited by 5
“This approach of distributed learning has become an efficient privacy-preserving learning model [2].”
BackgroundContributions of ML in Industry 5.0 to Sustainable Development
Uddin, M. S., Tanvir, M. H., Arafat, M. Y., & Jane, J. S. (2023) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-80455-640-520231007 · Cited by 5
Vukelić, Ž., Cajner, H., & Barić, G. (2024) · Tehnički glasnik · doi:10.31803/tg-20240502091110 · Cited by 4
“Within this concept, people are asked to develop skills and new competencies, both the necessary soft skills and acquiring advanced technical skills that can pose a problem for workers [30].”
BackgroundFederated learning for big data: A survey on opportunities, applications, and future directions
Gadekallu, T. R., Pham, Q., Huynh‐The, T., Feng, H., Fang, K., Pandya, S., Liyanage, M., Wang, W. W., & Nguyen, T. T. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.113614 · Cited by 86
“FL can be used for several other applications that are based on big data such as pollution control, defence etc. Liu et al. [131] proposed a novel FL based framework for monitoring and predicting the quality of the air.”
Prediction of the growth of fungal communities in different environments: A GomPertz model approach
Yang, F. (2021) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/784/1/012046 · Cited by 4
“The formula of prediction model is shown as follows: ( ) t k t t t L k Y a kb F + − + = + (10) In which, YYtt+kk is the prediction value of tt + kk time series, where kk ≥ 1, tt = nn × LL , nn is number of year.”
Method
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
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.
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