Simple Privacy-Preserving Federated Learning with Different Encryption Keys

Federated learning is a method where multiple participants collaboratively train a model using machine learning techniques, such as deep learning, while keeping each participant's data private through the use of a central server. However, there are cases in which participant input information is leaked to the server. To solve this problem, several protocols have been proposed. In particular, Phong et al. (in IEEE TIFS 2018) proposed a protocol that protects participant information against a server using homomorphic encryption. Later, Park et al. (in ICTC 2022) proposed an improved protocol, where each client has a different secret-key for the homomorphic encryption. In this paper, we show that the Park et al.'s protocol has some weakness and propose the secure one based on the previously proposed protocols.