Labels Are Culprits: Defending Gradient Attack on Privacy
Zhaohua Li, Le Wang, Zhaoquan Gu, Yang Lv, Zhihong Tian · IEEE Internet of Things Journal · 2023
Federated learning (FL) is widely studied for local privacy protection, and it involves exchanging model parameters rather than raw data among clients. However, gradient attacks (GAs) make a malicious client or parameter server of FL infer the local data of other clients only based on the model parameters exchanged. In FL frameworks and processes, it is important to understand the features that provide heuristic information for inferring raw data, as well as how best to defend against GAs. The academic community is currently investigating this problem. In this study, we demonstrate that the labels of input samples play a key role in the success of GAs. We analyze the rank of the coefficient matrix of the nonhomogeneous linear equation of gradients and input samples and propose an approach that performs special operations on the repetition and order of labels. The approach achieves a better defense effect against GAs without using a differential privacy (DP) framework. Our experimental results show that GAs fail (i.e., without leaking any valid information about local data) during the entire training process of a deep convolutional network in FL, and the accuracy of the network is less affected than that of DP. The code is available athttps://github.com/zhaohuali/Label-based-Defense.