Model Backdoor Attack on Federated Learning Based on Parameter Analysis

With the increasingly widespread application of federated learning (FL) in various fields, the issue of backdoor attacks against FL has garnered significant attention from both academia and industry. While there has been some progress in researching backdoor attacks against FL, data backdoor attacks are easily mitigated in federated environments, and model backdoor attacks are susceptible to detection by defense mechanisms. Therefore, we propose a refined backdoor attack method tailored for FL under the image classification task. Our method involves the collaborative operation of three key modules. Firstly, the parameter importance analysis module identifies parameters with minimal impact on model performance, and creates parameter importance masks to provide precise targets for subsequent operations. Subsequently, the activation difference computation module calculates the activation differences between backdoor samples and benign samples to locate trigger-sensitive parameters. Our method implants the backdoor by flipping and zeroing the precisely located layer parameters, while maintaining the model's classification performance on benign samples. Experimental results show that our method is feasible to achieve an average attack success rate more than 99% across the three victim models. This demonstrates the effectiveness of our method in FL environments and its robustness against various FL defense mechanisms.