  As a distributed machine learning paradigm, Federated Learning (FL) enables
large-scale clients to collaboratively train a model without sharing their raw
data. However, due to the lack of data auditing for untrusted clients, FL is
vulnerable to poisoning attacks, especially backdoor attacks. By using poisoned
data for local training or directly changing the model parameters, attackers
can easily inject backdoors into the model, which can trigger the model to make
misclassification of targeted patterns in images. To address these issues, we
propose a novel data-free trigger-generation-based defense approach based on
the two characteristics of backdoor attacks: i) triggers are learned faster
than normal knowledge, and ii) trigger patterns have a greater effect on image
classification than normal class patterns. Our approach generates the images
with newly learned knowledge by identifying the differences between the old and
new global models, and filters trigger images by evaluating the effect of these
generated images. By using these trigger images, our approach eliminates
poisoned models to ensure the updated global model is benign. Comprehensive
experiments demonstrate that our approach can defend against almost all the
existing types of backdoor attacks and outperform all the seven
state-of-the-art defense methods with both IID and non-IID scenarios.
Especially, our approach can successfully defend against the backdoor attack
even when 80\% of the clients are malicious.
