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

Claim 7:
8. The model parameter training method based on federation learning of claim 6, wherein after the operation of receiving and calculating a loss sum according to the first encryption loss value sent by the first terminal and the second encryption loss value sent by the second terminal, and determining whether the federation learning model to be trained is in a convergent state according to the loss sum, the method further comprises:
sending a continuing training instruction to the first terminal and the second terminal after determining that the federation learning model to be trained is in a non-convergent state;
receiving and calculating a gradient sum according to a first encryption gradient value sent by the first terminal and a second encryption gradient value sent by the second terminal, updating the first encryption federation learning model parameter according to the gradient sum to obtain a second encryption federation learning model parameter, sending the second encryption federation learning model parameter to the first terminal and the second terminal;
receiving and calculating a new loss sum according to a third encryption loss value send by the first terminal and a fourth encryption loss value sent by the second terminal, determining whether the federation learning model to be trained is in the convergent state according to the new loss sum, wherein the third encryption loss value is calculated by the first terminal according to the first sample, the first encryption supplementary sample, and the second encryption federation learning model parameter sent by the third terminal, and 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
if the federation learning model to be trained is in the convergent state, sending a stop training instruction to the first terminal and the second terminal, and using the second encryption federation learning model parameter as a final parameter of the federation learning model to be trained.