Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach

Understand

Federated Learning (FL) trains a black-box and high-dimensional model among different clients by exchanging parameters instead of direct data sharing, which mitigates the privacy leak incurred by machine learning.

• However, FL still suffers from membership inference attacks (MIA) or data reconstruction attacks (DRA).
• In particular, an attacker can extract the information from local datasets by constructing DRA, which cannot be effectively throttled by existing techniques, e.g., Differential Privacy (DP).
• In this paper, we aim to ensure a strong privacy guarantee for FL under DRA.