SPEFL: Efficient Security and Privacy-Enhanced Federated Learning Against Poisoning Attacks

Federated learning (FL) is a distributed machine learning paradigm in the Internet of Things (IoT), which allows multiple devices to collaboratively train models without leaking local data. In the open scenario of IoT, malicious devices can launch poisoning attacks to compromise the final model by s...

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Bibliographic Details
Published in:IEEE internet of things journal 2024-04, Vol.11 (8), p.13437-13451
Main Authors: Shen, Liyan, Ke, Zhenhan, Shi, Jinqiao, Zhang, Xi, Sun, Yanwei, Zhao, Jiapeng, Wang, Xuebin, Zhao, Xiaojie
Format: Article
Language:English
Subjects:
Computational modeling
Cryptography
Cybersecurity
Devices
Federated learning
Federated learning (FL)
Internet of Things
Internet of Things (IoT)
Machine learning
Model accuracy
Poisoning
poisoning attack
Privacy
privacy preserving
secure multiparty computation (MPC)
Servers
Training
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Summary:Federated learning (FL) is a distributed machine learning paradigm in the Internet of Things (IoT), which allows multiple devices to collaboratively train models without leaking local data. In the open scenario of IoT, malicious devices can launch poisoning attacks to compromise the final model by submitting crafted gradients. Some previous studies defend against poisoning attacks by analyzing the statistical characteristics of plaintext gradients. However, plaintext gradients would expose private information to malicious FL devices or servers. To simultaneously resist poisoning attacks and preserve privacy, cryptography technology can be utilized to obfuscate the gradients in defense methods, but the private calculation of resisting poisoning attack methods will cause efficiency problems, especially imposing unaffordable overhead on resource-limited IoT devices. Therefore, resisting poisoning attacks efficiently while protecting privacy remains a challenge. This article proposes a secure and privacy-enhanced FL (SPEFL) framework for efficient privacy-preserving and poisoning-resistant FL in IoT. We design an efficient secure computation protocol based on a three-server architecture to facilitate the cryptographic computation of large linear and complex nonlinear operators in the method against poisoning attacks. In SPEFL, most of the calculations are efficiently performed on the servers, which will not impose too much burden on resource-limited IoT devices. In addition, we design a security-enhanced verifiable protocol to detect the malicious behavior of the server and guarantee the correctness of FL aggregation results. Experimental and theoretical results demonstrate that SPEFL can efficiently complete FL training meanwhile guaranteeing the accuracy of the model.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2023.3339638