D²FL: Dimensional Disaster-oriented Backdoor Attack Defense Of Federated Learning

Defense algorithms for backdoor attacks in federated learning (FL) commonly rely on model parameter vectorization. However, as neural networks deepen, the exponential growth of model parameters leads to increased dimensionality, exacerbating the curse of dimensionality and reducing the effectiveness of traditional distance-based defenses. To address this, we propose Dimensional Disaster-oriented Backdoor Attack Defense in Federated Learning (D2FL), a method that mitigates attacks by focusing on expressive backdoor modules rather than the entire model. This approach reduces dimensionality and mitigates the challenges posed by large parameter spaces. Our extensive evaluation of D2FL on image classification tasks across various deep neural networks demonstrates its superior efficiency, significantly reducing both defense and aggregation times.