Combining Homomorphic Encryption and Differential Privacy in Federated Learning for Model Inspection and Availability

The increasing prevalence of decentralized data has led to a growing interest in federated learning, which enables collaborative model training without clients sharing their sensitive local data. However, FL alone does not sufficiently protect sensitive training data and is generally coupled with pr