Two-phase privacy-preserving scheme for federated learning in edge networks

In this article, to protect sharing parameters from being leaked and reduce time cost or low federated learning accuracy, we propose a two-phase privacy-preserving scheme for federated learning (TPPP-FL). Our method works in two phases. Specifically, in the uploading phase, we present a many-to-one HE algorithm to protect model parameters. In the downloading phase, to reduce the size of the keys, the zero-knowledge signature scheme is improved, and then a one-to-many zero-knowledge digital signature scheme is proposed to ensure the integrity and irreversibility of model parameters. Two datasets (MNIST and ORL) and two training models (CNN and MLP) have been set in experiments. Theoretical analysis and experimental results show that the TPPP-FL can effectively reduce time costs without losing the accuracy of the models compared to the same type of other federated learning schemes.