Joint modeling method and device

Abstract

The embodiment of the invention relates to the field of machine learning, in particular to a joint modeling method and device, which are used for improving model training efficiency and accuracy on the basis of protecting data security in a multi-party computing process. The method comprises the following steps: a first modeling node determines a first output result of first feature data based on a federated learning model, and sends a first encrypted output result obtained by encrypting the first output result to each second modeling node; the first modeling node receives a second encryption evaluation result sent by each second modeling node; the first modeling node determines a total encryption evaluation result of the federated learning model according to the first label data and each second encryption evaluation result, and decrypts the total encryption evaluation result to obtain a total model evaluation result; and the first modeling node decrypts the total encryption evaluation result to obtain a total evaluation result, determines an update gradient value, and updates a first model parameter of the first modeling node in the iteration process based on the update gradient value.

Description

technical field [0001] The invention relates to the field of machine learning, in particular to a joint modeling method and device. Background technique [0002] Federated learning is a machine learning framework that can help different organizations to jointly use and model data while meeting the requirements of user privacy protection, data security and government regulations. Specifically, federated learning needs to solve such a problem: on the premise that the data of each enterprise does not go out of the local area, a virtual shared model can be established through parameter exchange and optimization under the encryption mechanism. The performance of this common model is similar to the model trained by aggregating data from all parties. The data joint modeling scheme does not leak user privacy and complies with the principle of data security protection. [0003] In the existing scheme, only the modeling initiator can provide label data and initiate modeling, and the...

AI Technical Summary

Problems solved by technology

[0005] This application provides a joint modeling method and device to solve the problem that the number of samples is too small in the process of joint modeling and it is difficult to establish an effective federated learning model, and improve the accuracy of the federated learning model

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