FedGAT: a backdoor attack based on global model feedback optimized triggers in federated learning
TL;DR
A novel backdoor attack method, termed Federated Generative Adversarial Trigger (FedGAT), which adopts a Generative Adversarial Network (GAN) framework, and can automatically produce optimized triggers that are highly correlated with the global model’s feature space, effectively reducing the “loss” in backdoor transfer and improving attack performance.
Abstract
Federated learning (FL) has been widely applied in Internet of Things (IoT) systems. However, its inherently distributed nature makes it vulnerable to backdoor attacks. Existing attacks typically design triggers based on fixed patterns or local models, without considering their applicability to the global model. Consequently, a significant “loss” occurs during the transfer of the backdoor from the local model to the global model, leading to a low Attack Success Rate (ASR). To address this issue, we propose a novel backdoor attack method, termed Federated Generative Adversarial Trigger (FedGAT). The proposed method optimizes the trigger by training it with publicly available global models and a small number of samples. It adopts a Generative Adversarial Network (GAN) framework, in which the discriminator is fixed as the converged global model to guide the generator’s training through feedback. As a result, the generator can automatically produce optimized triggers that are highly correlated with the global model’s feature space, effectively reducing the “loss” in backdoor transfer and improving attack performance. Extensive experiments on multiple datasets demonstrate that, without amplifying malicious updates, the proposed method achieves an ASR improvement of 60.95–80.19% over the average baseline performance on the global model, exhibiting outstanding attack capability. Our work raises new challenges for the secure deployment of FL systems.