Federation modeling device and method based on privacy protection and readable storage medium

Abstract

The invention discloses a federation modeling method based on privacy protection. The federation modeling method comprises the following steps: aligning local sample data of each modeling node to construct a training sample; initializing to-be-trained model parameters of each modeling node; creating an encryption key pair, and sending the public key to each modeling node; controlling each modelingnode to encrypt according to the public key, and interacting with an intermediate result for calculating an encryption gradient and encryption loss; receiving a joint encryption loss summarized and calculated by a specified modeling node; distributing the joint encryption sample weight summarized and calculated by the specified modeling node to other modeling nodes to calculate an encryption gradient; decrypting the encryption gradient calculated by each modeling node; and returning the decrypted gradient to each modeling node so as to update model parameters for training until the joint lossfunction is converged. The invention further provides a federation modeling device based on privacy protection and a computer readable storage medium. According to the invention, joint modeling can be carried out under the condition that the data of each modeling node is not leaked.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a privacy protection-based federated modeling device, method and computer-readable storage medium. Background technique [0002] Using the trained machine learning model to predict unknown parameters or results is a very common technical means in the field of artificial intelligence. A single node often has a small number of samples, which leads to the problem of low prediction accuracy of the trained model. Therefore, joint modeling of multiple nodes to build a detection model is an important means to solve the lack of samples. However, the local sample data of different nodes often contain sensitive data, which is difficult to share from the perspective of privacy protection, which is not conducive to joint modeling. Contents of the invention [0003] In view of this, it is necessary to provide a privacy protection-based federated modeling device, method and ...

AI Technical Summary

Problems solved by technology

A single node often has a small number of samples, which leads to the problem of low prediction accuracy of the trained model. Therefore, joint modeling of multiple nodes to build a detection model is an important means to solve the lack of samples

However, the local sample data of different nodes often contain sensitive data. From the perspective of privacy protection, these data are difficult to share, which is not conducive to joint modeling

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