Federated learning methods and apparatuses, readable storage media, and electronic devices

Abstract

This application relates to methods, readable storage media and apparatuses for federated learning. In an example, a first aspect program is injected at a gradient sending function of a federated learning device by using a pre-deployed aspect framework. A to-be-trained model is trained based on local training data to obtain a plaintext gradient of the to-be-trained model. The plaintext gradient is sent to a federated learning server by using the gradient sending function. The plaintext gradient is intercepted and encrypted by using the first aspect program to obtain a ciphertext gradient. The ciphertext gradient is sent to the federated learning server by using the first aspect program, so that the federated learning server decrypts received ciphertext gradients sent by federated learning devices, and updates parameters of the to-be-trained model based on the plaintext gradients obtained after the decryption.