PDLG: Prevent Deep Leakage from Gradients based on Dataset Condensation in Federated Meta-Learning

Federated Meta-Learning (FML) has achieved privacy protection and intelligent improvement in many fields, such as healthcare and finance. However, the small dataset characteristic of FML may satisfy the strong assumptions of Deep Leakage from Gradients (DLG) attacks, making it susceptible to such attacks. Previous research has considered the strong assumptions of DLG to be impractical in real-world scenarios, resulting in a gap in the defense against DLG attacks in FML scenarios. In this paper, we propose a method called Prevent Deep Leakage Gradients (PDLG), PDLG utilizes condensation of training dataset to prevent DLG in FML, addressing the strong assumption problem of DLG in real-world scenarios. Specifically, PDLG first condenses the local raw training data through the client. Second use condensation data instead of the raw training data to train the model, and evaluate the changes in model classification performance in FML. Third DLG is used for the condensed dataset. As the Mean Square Error (MSE) between the real and the dummy gradients decreased, DLG could not restore the original images. The experimental results demonstrated that the classification performance of the models trained on condensation data remained within 10%. PDLG achieving a balance between model performance and privacy.