Federated learning multi-party security calculation method and device

A computing method and federated technology, applied in the field of machine learning, can solve problems such as the difficulty of fully mining the value of multi-party data, low user overlap, and poor data efficiency

Pending Publication Date: 2021-01-05
CHINA UNIONPAY
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  • Claims
  • Application Information

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Problems solved by technology

However, due to the low degree of user overlap between different platforms, there are problems such as poor

Method used

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  • Federated learning multi-party security calculation method and device
  • Federated learning multi-party security calculation method and device
  • Federated learning multi-party security calculation method and device

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Experimental program
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specific Embodiment 2

[0123] The scenario of the second embodiment is that federated learning based on user group alignment is used in the research and development of the medical dispatching system. The purpose is to reduce the waiting time of the user while satisfying the satisfaction of the user's diagnosis and treatment, and recommend the most suitable hospital and department for the user to register. , to maximize overall user satisfaction. Cooperating with various hospitals for multi-federal learning not only protects the privacy of users, but also allocates social medical resources reasonably. Process such as Figure 5 shown, including:

[0124] The users on the payment (UnionPay) side and the hospital side are subdivided into multiple categories according to the attributes negotiated by both parties based on the basic attributes (age, gender, region, purchasing power, etc.) For user groups, assign a group ID to each user group, and extract features for each user group to form a feature dat...

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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...

Claims

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Application Information

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IPC IPC(8): G06F16/9535G06F21/60G06F21/64G06N20/10
CPCG06F21/602G06F21/64G06F16/9535G06N20/10
Inventor 陈滢汤韬高鹏飞赵金涛郑建宾贡兆金潘婧刘红宝
Owner CHINA UNIONPAY
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