Defense Against Gradient Leakage Attacks via Learning to Obscure Data

Understand

Federated learning is considered as an effective privacy-preserving learning mechanism that separates the client's data and model training process.

• However, federated learning is still under the risk of privacy leakage because of the existence of attackers who deliberately conduct gradient leakage attacks to reconstruct the client data.
• Recently, popular strategies such as gradient perturbation methods and input encryption methods have been proposed to defend against gradient leakage attacks.
• Nevertheless, these defenses can either greatly sacrifice the model performance, or be evaded by more advanced attacks.