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

Claim 2:
3. The model parameter training method based on federation learning of claim 1, wherein after the operation of 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, the method further comprises:
calculating and sending a first encryption gradient value corresponding to the first encryption loss value to the third terminal after receiving a continuing training instruction sent by the third terminal, for the third terminal to calculate a gradient sum according to the first encryption gradient value and a second encryption gradient value, updating the first encryption federation learning model parameter according to the gradient sum to obtain a second encryption federation learning model parameter, wherein the second encryption gradient 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, the continuing training instruction is sent by the third terminal after determining that the federation learning model to be trained is in a non-convergent state;
obtaining the second encryption federation learning model parameter sent by the third terminal, and calculating a third encryption loss value of the first terminal according to the second encryption federation learning model parameter;
sending the third encryption loss value to the third terminal for the third terminal to calculate a new loss sun according to the third encryption loss value and a fourth encryption loss value, determining whether the federation learning model to be trained is in the convergent state according to the new loss sum, wherein the fourth encryption loss value is calculated by the second terminal according to the second sample, the second encryption supplementary sample, and the second encryption federation learning model parameter sent by the third terminal; and
using the second 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.