Data privacy protection method, server device and client device for federated learning

Abstract

The disclosure provides a data privacy protection method, a server device, and a client device for federated learning. A public dataset is used to perform model training on a machine learning model by a server device to generate a gradient pool including multiple first gradients. The gradient pool and the machine learning model are received by a client device. The client device uses a local dataset to perform model training on the machine learning model to obtain a second gradient. A local gradient is selected from the first gradients in the gradient pool according to the second gradient using a differential privacy algorithm by the client device. An aggregated machine learning model is generated by performing model aggregation based on the local gradient by the server device.