FLUDP: A Backdoor Attack Defense Framework in Federated Learning

Federated learning(FL) is a popular multi-party collaborative privacy computing method. Due to the distributed characteristics of its client, it is more vulnerable to backdoor attacks. In this attack, the attacker will inject the manipulated model update into the aggregation process of the federated model, so that the final generated model can provide targeted wrong prediction for the specific input selected by the attacker. Current defense methods against backdoor attacks mainly focus on detecting and filtering malicious client model updates. These defense methods adopt HDBSCAN clustering, K-means clustering, etc, which generally fail to defense against backdoor attacks in decoy models that contain indicators and there is no obvious clipping threshold for benign groups further clipping after clustering defense. To address these shortcomings, we propose the FLUDP method, which uses UMAP Magnify the angular difference between updates, DBSCAN clustering technology, and a clear threshold clipping technique. We have evaluated FLUDP on MNIST, Tiny-Imagenet, and CIFAR10 datasets. FLUDP achieves lower ABA and EBA rates and maintains a good MA rate compared to previous defense methods.