  Federated Learning (FL) is a widely adopted privacy-preserving machine
learning approach where private data remains local, enabling secure
computations and the exchange of local model gradients between local clients
and third-party parameter servers. However, recent findings reveal that privacy
may be compromised and sensitive information potentially recovered from shared
gradients. In this study, we offer detailed analysis and a novel perspective on
understanding the gradient leakage problem. These theoretical works lead to a
new gradient leakage defense technique that secures arbitrary model
architectures using a private key-lock module. Only the locked gradient is
transmitted to the parameter server for global model aggregation. Our proposed
learning method is resistant to gradient leakage attacks, and the key-lock
module is designed and trained to ensure that, without the private information
of the key-lock module: a) reconstructing private training data from the shared
gradient is infeasible; and b) the global model's inference performance is
significantly compromised. We discuss the theoretical underpinnings of why
gradients can leak private information and provide theoretical proof of our
method's effectiveness. We conducted extensive empirical evaluations with a
total of forty-four models on several popular benchmarks, demonstrating the
robustness of our proposed approach in both maintaining model performance and
defending against gradient leakage attacks.
