Mitigating Leakage in Federated Learning with Trusted Hardware

Understand

In federated learning, multiple parties collaborate in order to train a global model over their respective datasets.

• Even though cryptographic primitives (e.g., homomorphic encryption) can help achieve data privacy in this setting, some partial information may still be leaked across parties if this is done non-judiciously.
• In this work, we study the federated learning framework of SecureBoost [Cheng et al., FL@IJCAI'19] as a specific such example, demonstrate a leakage-abuse attack based on its leakage profile, and experimentally evaluate the effectiveness of our attack.
• We then propose two secure versions relying on trusted execution environments.