R-GAP: Recursive Gradient Attack on Privacy

Understand

Federated learning frameworks have been regarded as a promising approach to break the dilemma between demands on privacy and the promise of learning from large collections of distributed data.

• Many such frameworks only ask collaborators to share their local update of a common model, i.e.
• gradients with respect to locally stored data, instead of exposing their raw data to other collaborators.
• However, recent optimization-based gradient attacks show that raw data can often be accurately recovered from gradients.