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

Claim 5:
6. A model parameter training method based on federation learning, applied to a third terminal, comprising the following operations:
after a first terminal uses a second encryption mapping model of a second terminal to predict a missing feature of a first sample of the first terminal to obtain a first encryption supplementary sample, and the second terminal uses a first encryption mapping model of the first terminal to predict a missing feature of a second sample of the second terminal to obtain a second encryption supplementary sample, sending, by the third terminal, a first encryption federation learning model parameter to the first terminal and the second terminal, for the first terminal to calculate a first encryption loss value according to the first encryption federation learning model parameter, the first sample and the first encryption supplementary sample, and for the second terminal to calculate a second encryption loss value according to the first encryption federation learning model parameter, the second sample and the second encryption supplementary sample, wherein the second encryption mapping model is obtained by training the second sample by a feature intersection of the first sample and the second sample, the first encryption mapping model is obtained by training the first sample by the feature intersection;
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; and
sending the first encryption federation learning model parameter as a final parameter of the federation learning model to be trained after determining that the federation learning model to be trained is in the convergent state.