A Secure Federated Learning Framework for 5G Networks

Liu, Yi; Peng, Jialiang; Kang, Jiawen; Iliyasu, Abdullah M.; Niyato, Dusit; El-Latif, Ahmed A. Abd

doi:10.1109/mwc.01.1900525

Cited by 291Privacy-Preserving Technologies in DataBlockchain Technology Applications and SecurityAdversarial Robustness in Machine Learning

A Secure Federated Learning Framework for 5G Networks

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Liu, Yi, Peng, Jialiang, Kang, Jiawen, Iliyasu, Abdullah M., Niyato, Dusit, El-Latif, Ahmed A. Abd·2020·IEEE Wireless Communications

DOI 10.1109/mwc.01.1900525

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Abstract

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 proposed blockchain-based federated learning protocol can deter poisoning attacks.
“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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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)
ORCID: Liu, Yi
Peng, Jialiang
Kang, Jiawen
Iliyasu, Abdullah M.
Niyato, Dusit
Venue: IEEE Wireless Communications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-08
Volume: 27
Issue: 4
Pages: 24-31
ISSN: 1536-1284 (ISSN-L)
1558-0687
DOI: 10.1109/mwc.01.1900525
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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A Secure Federated Learning Framework for 5G Networks

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