The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
A Secure Federated Learning Framework for 5G Networks
Open accessAbstract
Federated learning (FL) has recently been proposed as an emerging paradigm to build machine learning models using distributed training datasets that are locally stored and maintained on different devices in 5G networks while providing privacy preservation for participants. In FL, the central aggregator accumulates local updates uploaded by participants to update a global model. However, there are two critical security threats: poisoning and membership inference attacks. These attacks may be carried out by malicious or unreliable participants, resulting in the construction failure of global models or privacy leakage of FL models. Therefore, it is crucial for FL to develop security means of defense. In this article, we propose a blockchain-based secure FL framework to create smart contracts and prevent malicious or unreliable participants from being involved in FL. In doing so, the central aggregator recognizes malicious and unreliable participants by automatically executing smart contracts to defend against poisoning attacks. Further, we use local differential privacy techniques to prevent membership inference attacks. Numerical results suggest that the proposed framework can effectively deter poisoning and membership inference attacks, thereby improving the security of FL in 5G networks.
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“The performance evaluation using two datasets, including, MNIST dataset and CIFAR-10 dataset, show that the proposed protocol can deter poisoning attacks.”
From Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis (2021) · cites this paper as [101]
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Communication-Efficient Learning of Deep Networks from Decentralized Data
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McMahan, H. B., Moore, E., Ramage, D., & y Arcas, B. A. (2016) · arXiv (Cornell University) · Cited by 1,005
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Self-citationDeepChain: Auditable and Privacy-Preserving Deep Learning with Blockchain-based Incentive
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“In a word, the current FL models rely on a trust mechanism, which makes it vulnerable to suffer from poisoning attacks [14].”
BackgroundGANobfuscator: Mitigating Information Leakage Under GAN via Differential Privacy
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Self-citationSDN-based Mitigation of Scanning Attacks for the 5G Internet of Radio Light System
Cabaj, K., Gregorczyk, M., Mazurczyk, W., Nowakowski, P., & Żórawski, P. (2018) · International Conference on Availability, Reliability and Security (ARES) · doi:10.1145/3230833.3233248 · Cited by 19
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A 5G Radio-Light SDN Architecture for Wireless and Mobile Network Access in Buildings
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Mobile Communications and Networks
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Mobile Communications and Networks
Alexander, T., Mazurczyk, W., Mishra, A., & Perotti, A. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.2019.8847237 · Cited by 4
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Membership Inference Attacks on Machine Learning: A Survey
Hu, H., Salcic, Z. A., Sun, L., Dobbie, G., Yu, P. S., & Zhang, X. (2022) · ACM Computing Surveys · doi:10.1145/3523273 · Cited by 662
Federated learning for 6G communications: Challenges, methods, and future directions
Liu, Y., Yuan, X., Xiong, Z., Kang, J., Wang, X., & Niyato, D. (2020) · China Communications · doi:10.23919/jcc.2020.09.009 · Cited by 464
“For 6G networks, intelligent applications are one baseline of application requirements [9], [11].”
BackgroundMethodInfluentialSelf-citationJia, B., Zhang, X., Liu, J., Zhang, Y., Huang, K., & Liang, Y. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3085960 · Cited by 358
Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342
Asynchronous federated learning on heterogeneous devices: A survey
Xu, C., Qu, Y., Xiang, Y., & Gao, L. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100595 · Cited by 406
Metadata
- Authors
- Liu, Y., Peng, J., Kang, J., Iliyasu, A. M., Niyato, D., & El-Latif, A. A. A.
- Affiliations
- Heilongjiang University (CN)
- Nanyang Technological University (SG)
- Changchun University of Science and Technology (CN)
- Venue
- IEEE Wireless Communications
- Published
- 2020-08
- Volume
- 27
- Issue
- 4
- Pages
- 24-31
- ISSN
- 1536-1284 (ISSN-L)
- 1558-0687
- OpenAlex
- W3025412407
- Cited by
- 290
- References
- 17
- Citations by year
- 2026: 19 · 2025: 39 · 2024: 50 · 2023: 67 · 2022: 58 · 2021: 53 · 2020: 4
- FWCI
- 17.87
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Wireless Communications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- federated learning, 5G, blockchain, smart contracts, poisoning attacks, membership inference attacks, differential privacy, privacy preservation
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