Federated-learning based method of acquiring model parameters, system and readable storage medium

A system of acquiring a model parameter, comprising a memory, a processor and a model program for acquiring the model parameter based on federated learning which is stored in the memory and can be executable on the processor, and when executed by the processor, the program implements the following operations:

• calculating first data of a first terminal and second data of a second terminal to obtain a loss value;
• encrypting, by the second terminal, the loss value;
• sending, by the second terminal, the encrypted loss value to a third terminal;
• receiving, by the second terminal, the encrypted loss value sent by the second terminal, and decrypting the encrypted loss value to obtain the loss value;
• detecting whether the model to be trained is at convergence according to the loss value after decrypting;
• in response that the model to be trained is at convergence, acquiring a gradient corresponding to the loss value;
• and
• determining a sample parameter corresponding to the gradient, and determining the sample parameter as a model parameter of the model to be trained.