Model parameter obtaining method and system based on federation learning and readable storage medium

Abstract

The invention discloses a model parameter obtaining method and system based on federation learning, and a readable storage medium. The method comprises the following steps: when a third terminal receives an encrypted loss value sent by a second terminal, the third terminal decrypts the loss value to obtain the decrypted loss value; whether the model to be trained is in a convergent state accordingto the decrypted loss value is detected; if the model to be trained is detected to be in a convergent state, a gradient value corresponding to the loss value is obtained; the sample parameters corresponding to the gradient value are determined, and the sample parameters corresponding to the gradient value are determined as the model parameters of the model to be trained. The invention realizes that the loss value is calculated by combining the sample data of the first terminal and the second terminal, so as to help to learn and determine the model parameters in the model to be trained by combining the sample data of the first terminal and the second terminal, and improves the accuracy of the trained model.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a federated learning-based model parameter acquisition method, system and readable storage medium. Background technique [0002] With the rapid development of machine learning, machine learning can be applied in various fields, such as data mining, computer vision, natural language processing, biometric identification, medical diagnosis, detection of credit card fraud, securities market analysis and DNA (deoxyribonucleic acid, deoxyribonucleic acid ) sequence sequencing, etc. In machine learning, the system usually provides sample data, and the learning part uses the sample data to modify the knowledge base of the system to improve the performance of the execution part of the system to complete the task. The execution part completes the task according to the knowledge base and feeds back the obtained information to the learning part. [0003] At present, due to t...

AI Technical Summary

Problems solved by technology

[0004] The main purpose of the present invention is to provide a model parameter acquisition method, system and readable storage medium based on federated learning, aiming to solve the existing technical problem of how to combine data from all parties and improve the accuracy of the obtained model

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