Patent ID: 11947680
Assignee: WEBANK CO., LTD
Field: Computer technology (Electrical engineering)
Classification: CPC G  Y | IPC G

Claim 0:
1. A model parameter training method based on federation learning, applied to a first terminal, comprising the following operations:
determining a feature intersection of a first sample of the first terminal and a second sample of a second terminal, training the first sample based on the feature intersection to obtain a first mapping model, encrypting and sending the first mapping model to the second terminal, for the second terminal to predict a missing feature of the second sample to obtain a second encryption supplementary sample;
receiving a second encryption mapping model sent by the second terminal, predicting a missing feature of the first sample of the first terminal according to the second encryption mapping model to obtain a first encryption supplementary sample, wherein the second encryption mapping model is obtained by the second terminal training the second sample based on the feature intersection;
receiving a first encryption federation learning model parameter sent by a third terminal, training a federation learning model to be trained according to the first encryption federation learning model parameter, the first sample and the first encryption supplementary sample, and calculating a first encryption loss value;
sending the first encryption loss value to the third terminal, for the third terminal to calculate a loss sum according to the first encryption loss value and a second encryption loss value, determining whether the federation learning model to be trained is in a convergent state according to the loss sum, wherein the second encryption loss value is calculated by the second terminal according to the second sample, the second encryption supplementary sample, and the first encryption federation learning model parameter sent by the third terminal; and
using the first encryption federation learning model parameter as a final parameter of the federation learning model to be trained after receiving a stop training instruction sent by the third terminal, wherein the stop training instruction is sent by the third terminal after determining that the federation learning model to be trained is in the convergent state.