Combining Homomorphic Encryption and Differential Privacy in Federated Learning for Model Inspection and Availability
The research proposes combining Homomorphic Encryption (HE) for training utility with Differential Privacy (DP) for private model inspection and release in Federated Learning. The method allows monitoring model updates during training without altering the core encryption, achieving better privacy guarantees and higher model accuracy than using DP alone.