Detecting Free-Riding Attack in Federated Learning Based on Gradient Backtracking

Hong Zhen; Feng Wanglei; Wen Zhenyu; Wu Di; Li Taotao; Wu Yiming; Wang Cong; Ji Shouling

doi:10.7544/issn1000-1239.202330886

Journal of Computer Research and Development > 2024 > 61(9): 2185-2198. > DOI: 10.7544/issn1000-1239.202330886 CSTR: 32373.14.issn1000-1239.202330886

Hong Zhen, Feng Wanglei, Wen Zhenyu, Wu Di, Li Taotao, Wu Yiming, Wang Cong, Ji Shouling. Detecting Free-Riding Attack in Federated Learning Based on Gradient Backtracking[J]. Journal of Computer Research and Development, 2024, 61(9): 2185-2198. DOI: 10.7544/issn1000-1239.202330886

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Detecting Free-Riding Attack in Federated Learning Based on Gradient Backtracking

Hong Zhen^{1, 2,},
Feng Wanglei¹,
Wen Zhenyu^{1, 2},
Wu Di^3,,
Li Taotao¹,
Wu Yiming^{1, 2, ,},
Wang Cong⁴,
Ji Shouling⁵

1.
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023
2.
Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023
3.
College of Computer Science, University of St Andrews, St Andrews KY16 9AJ
4.
College of Control Science and Engineering, Zhejiang University, Hangzhou 310007
5.
College of Computer Science and Technology, Zhejiang University, Hangzhou 310007

Funds: This work was supported by the National Natural Science Foundation of China (62072408，62302454), the Natural Science Foundation of Zhejiang Province for Distinguished Young Scholars (LR24F020004), the Major Program of the Natural Science Foundation of Zhejiang Province (Youth Original Project) (LDQ24F020001), and the China Postdoctoral Science Foundation (2023M743403).

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Author Bio:
Hong Zhen: born in 1983. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include Internet of things/information physical systems, intelligent systems security, big data analytics, and artificial intelligence

Feng Wanglei: born in 1997. Master candidate. His main research interests include federated learning and distributed machine learning

Wen Zhenyu: born in 1987. PhD, professor, PhD supervisor. Member of CCF. His main research interests include IoT, crowd sources, AI system, and cloud computing

Wu Di: born in 1993. PhD candidate. His main research interests include federated learning, distributed machine learning, edge computing, model compression, and Internet-of-Things

Li Taotao: born in 1996. PhD candidate. His main research interests include Web mining, information retrieval, machine learning

Wu Yiming: born in 1996. PhD, associate professor, master supervisor. Member of CCF. Her main research interests include data-driven security, black industry mining, and cybercrime research

Wang Cong: born in 1985. PhD, professor, PhD supervisor. Member of CCF. His main research interests include addressing security and privacy challenges in mobile, cloud computing, IoT, and machine learning and system

Ji Shouling: born in 1986. PhD, professor, PhD supervisor. Senior member of CCF. His main research interests include data-driven security and privacy, AI security, and big data mining and analytics
Received Date: October 31, 2023
Revised Date: May 19, 2024
Available Online: May 29, 2024

Graphical Abstract

Abstract

Abstract

With the development of the Internet of vehicles (IoV), the rapid growth of intelligent vehicles generates a massive amount of data. These data are invaluable for training intelligent IoV application models. Traditional model training requires the centralized collection of raw data through the cloud, consuming substantial communication resources and facing issues like privacy breaches and regulatory constraints. Federated learning (FL) offers a solution by using model transfer instead of data transfer to tackle these challenges. However, practical FL systems are confronted with the issue of malicious users attempting to deceive the server by uploading false local models, known as free-riding attacks. These attacks significantly undermine the fairness and effectiveness of FL. Current research assumes that free-riding attacks are limited to a small number of rational users. However, when there are multiple malicious free-riders, current research falls short in effectively detecting and defending against these attackers. To address this issue, we introduce a novel gradient backtracking based algorithm to identify free-riders. We introduce random testing rounds into standard FL and compare the similarity of user’s gradient between the testing round and the comparison round. It overcomes the challenge of ineffective defense in scenarios involving multiple malicious free-riders. Experimental results on the MNIST and CIFAR-10 datasets demonstrate that the proposed detection algorithm achieves outstanding performance in various free-riding attack scenarios.
- federated learning,
- Internet of vehicles,
- free-riding attack,
- gradient similarity,
- free-riding attack detection

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References (27)

References

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