Gradient Obfuscation Gives a False Sense of Security in Federated Learning

Understand

Federated learning has been proposed as a privacy-preserving machine learning framework that enables multiple clients to collaborate without sharing raw data.

• However, client privacy protection is not guaranteed by design in this framework.
• Prior work has shown that the gradient sharing strategies in federated learning can be vulnerable to data reconstruction attacks.
• In practice, though, clients may not transmit raw gradients considering the high communication cost or due to privacy enhancement requirements.