Federated learning multi-party security calculation method and device

Abstract

The embodiment of the invention relates to the field of machine learning, in particular to a federated learning multi-party security computing method and a device, and aims to improve the model training efficiency and accuracy on the basis of protecting the data security in a multi-party computing process. The method comprises the steps that adopting a data node to divide all individual objects inthe data node into a plurality of object sets according to classification standards; for each object set, adopting the data node to determine feature data of the object set according to the feature data of all the individual objects in the object set; adopting the data node to send the feature data of the object set to a model node, so that the model node performs sample alignment on all the feature data of the same object set according to the feature data of the object set sent by the plurality of data nodes to obtain sample data of the object set, training the federated learning model according to the sample data of all the object sets; wherein the classification standards for dividing the individual objects in the plurality of data nodes are the same.

Description

technical field [0001] The invention relates to the field of machine learning, in particular to a federated learning multi-party secure computing 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] When the current federated learning performs modeling training on the mo...

AI Technical Summary

Problems solved by technology

However, due to the low degree of user overlap between different platforms, there are problems such as poor data efficiency, low calculation efficiency, and difficulty in fully mining the value of multi-party data.

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