Edge-Cloud Collaborative Defense against Backdoor Attacks in Federated Learning

Jie Yang, Jun Zheng, Haochen Wang, Jiaxing Li, Haipeng Sun, Weifeng Han, Nan Jiang, Yu An Tan*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

3 引用 (Scopus)

摘要

Federated learning has a distributed collaborative training mode, widely used in IoT scenarios of edge computing intelligent services. However, federated learning is vulnerable to malicious attacks, mainly backdoor attacks. Once an edge node implements a backdoor attack, the embedded backdoor mode will rapidly expand to all relevant edge nodes, which poses a considerable challenge to security-sensitive edge computing intelligent services. In the traditional edge collaborative backdoor defense method, only the cloud server is trusted by default. However, edge computing intelligent services have limited bandwidth and unstable network connections, which make it impossible for edge devices to retrain their models or update the global model. Therefore, it is crucial to detect whether the data of edge nodes are polluted in time. This paper proposes a layered defense framework for edge-computing intelligent services. At the edge, we combine the gradient rising strategy and attention self-distillation mechanism to maximize the correlation between edge device data and edge object categories and train a clean model as much as possible. On the server side, we first implement a two-layer backdoor detection mechanism to eliminate backdoor updates and use the attention self-distillation mechanism to restore the model performance. Our results show that the two-stage defense mode is more suitable for the security protection of edge computing intelligent services. It can not only weaken the effectiveness of the backdoor at the edge end but also conduct this defense at the server end, making the model more secure. The precision of our model on the main task is almost the same as that of the clean model.

源语言英语
文章编号1052
期刊Sensors
23
3
DOI
出版状态已出版 - 2月 2023

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引用此

Yang, J., Zheng, J., Wang, H., Li, J., Sun, H., Han, W., Jiang, N., & Tan, Y. A. (2023). Edge-Cloud Collaborative Defense against Backdoor Attacks in Federated Learning. Sensors, 23(3), 文章 1052. https://doi.org/10.3390/s23031052