.png){kind=icon}
.png){kind=icon}
Return
A gradient inversion attack defense method based on data augmentation
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-025-06533-y.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The gradient inversion attack presents a significant threat to the data privacy in federated learning, enabling malicious adversaries to reconstruct private training data from gradients. Among the various protection strategies, data augmentation-based approaches have emerged as particularly promising. These methods can be seamlessly incorporated into existing federated learning frameworks, offering both efficiency and minimal impact on model accuracy. In this paper, we propose a novel data protection technique that leverages data augmentation methods, specifically CutMix and SaliencyMix. These techniques work by mixing images, which allows for more efficient utilization of training pixels. This, in turn, aids the model in learning more robust and meaningful feature representations, thereby enhancing both the model performance and its resilience to adversarial attacks. To further strengthen data privacy, we integrate these data augmentation methods with data pruning techniques. Our empirical results demonstrate that the proposed approach not only improves the accuracy of federated learning models but also reduces the quality of reconstructed images, offering a higher level of data privacy protection.
Keywords:
Federated learning
Data privacy
Data augmentation
Gradient inversion attack
Journal
IF:
3.5
Papers:
7.5K
Citations:
1.7W