On Securing Data Privacy in Federated Learning Using Noise-assisted Aggregated Multi-key Homomorphic Encryption

Yuxiao Ma, Wei Lou, Song Guo

Abstract: Federated learning (FL) is becoming increasingly popular due to concerns about data privacy. In FL, multiple clients and a server jointly train a model. Clients update their models locally and send only the updated parameters to the server rather than raw data, which helps protect data privacy to a certain extent. However, research shows that even when the server or other clients know a client’s parameter data, adversaries can recover a lot of private information about data owners through inference attacks. In addition, adversaries can also obtain private information from aggregated parameters. To solve these problems, we propose a new privacy-preserving scheme in cross-silo FL based on aggregated multi-key BFV homomorphic encryption and noise addition. In this work, we introduce an aggregated multi-key BFV homomorphic encryption method to protect data privacy for each single transmission and a noise addition method to enhance privacy protection for aggregated parameters. Our scheme protects data privacy against collusion between the server and up to N − 2 clients without relying on a trusted third party, where N is the total number of clients participating in the training process. The experiments show that our noise-assisted aggregated multi-key BFV homomorphic encryption method achieves the same model performance as a plain federated averaging algorithm with the same machine learning models while providing higher computational and communication efficiency compared to two other state-of-the-art methods.