Open access
Aug 2026
FedGAT: a backdoor attack based on global model feedback optimized triggers in federated learning
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.
Tao Liu, Ji-Guang Lv, D. Man et al.
· Cybersecurity · 0 citations