Federal learning model training method and system, electronic equipment and readable storage medium

A technology for learning models and training methods, applied in the field of data security protection, can solve problems such as illegal collection of user privacy data, meet the needs of privacy protection and data security, and promote fair cooperation.

Pending Publication Date: 2021-03-12
BEIJING XUEZHITU NETWORK TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In China, users’ protection of data privacy is not so strong, and applications require a lot of permissions, but overseas, especially in European countries, GDPR is extremely strict, and it is illegal to collect user privacy data

Method used

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  • Federal learning model training method and system, electronic equipment and readable storage medium
  • Federal learning model training method and system, electronic equipment and readable storage medium
  • Federal learning model training method and system, electronic equipment and readable storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0059] refer to figure 1 as shown, figure 1 It is a schematic diagram of the steps of the federated learning model training method for solving the data privacy problem in the recommendation system provided by the present invention. Such as figure 1 As shown, this embodiment discloses a specific implementation manner of a federated learning model training method (hereinafter referred to as "method") for solving the data privacy problem in the recommendation system.

[0060] Specifically, recommender systems can be divided into two categories: collaborative filtering-based (CFB) recommender systems and content-based (CB) recommender systems. CFB recommends items with similar preferences to a specific user based on the similarity between users. CB performs recommendation based on the nature of items, which can be recommended by certain explicit characteristics such as attributes and characteristics.

[0061] Recommending a privacy-preserving scheme for CFB: usually a privacy-...

Embodiment 2

[0103] In combination with a federated learning model training method for solving the data privacy problem in the recommendation system disclosed in Embodiment 1, this embodiment discloses a federated learning model training system for solving the data privacy problem in the recommendation system (hereinafter referred to as Examples of specific implementations of the "system").

[0104] refer to Figure 5 As shown, the system includes:

[0105] Matrix download module 11: download the global material factor matrix from the server;

[0106] Data set upload module 12: based on the local data, the global material factor matrix and the local user factor vector, the data set is updated and uploaded to the server;

[0107] Matrix sending module 13: the server updates the global material factor matrix based on the federated weighting algorithm and the updated local model and sends it to the user.

[0108] Specifically, the global material factor matrix in the matrix downloading mod...

Embodiment 3

[0121] combine Figure 6 As shown, this embodiment discloses a specific implementation manner of a computer device. The computer device may comprise a processor 81 and a memory 82 storing computer program instructions.

[0122] Specifically, the processor 81 may include a central processing unit (CPU), or an Application Specific Integrated Circuit (ASIC for short), or may be configured to implement one or more integrated circuits in the embodiments of the present application.

[0123] Among them, the memory 82 may include mass storage for data or instructions. For example without limitation, the memory 82 may include a hard disk drive (Hard Disk Drive, referred to as HDD), a floppy disk drive, a solid state drive (SolidState Drive, referred to as SSD), flash memory, optical disk, magneto-optical disk, magnetic tape or universal serial bus (Universal Serial Bus, referred to as USB) drive or a combination of two or more of the above. Storage 82 may comprise removable or non-r...

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PUM

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Abstract

The invention discloses a federated learning model training method and system, electronic equipment and a readable storage medium. The method comprises the steps that a global material factor matrix is downloaded from a server; the data set is updated based on the local data, the global material factor matrix and the local user factor vector and uploaded to the server; and the server updates the global material factor matrix based on a federated weighting algorithm and the updated local model and sends the updated global material factor matrix to the user. In the whole modeling process, data sharing and data privacy protection are effectively achieved, and encryption exchange of information is carried out under the condition that it is guaranteed that two participating parties are kept independent.

Description

technical field [0001] The present invention relates to the technical field of data security protection, in particular to a federated learning model training method, system, electronic device and readable storage medium for solving data privacy problems in a recommendation system. Background technique [0002] The recommendation system needs to predict the user's future behavior and interest based on the user's historical behavior and interest, so a large amount of user behavior data becomes an important part and prerequisite of the recommendation system. User data hiding is a major challenge in the field of recommender systems. [0003] Most of the existing privacy-preserving personalized recommendation services assume that users participate in the whole process honestly. However, there are two situations that are not considered. One situation is the occurrence of malicious users. For example, some users may deliberately provide invalid data to recommenders to damage the s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/60G06F21/62H04L29/06G06N20/00G06F16/9535G06F16/9536
CPCG06F21/602G06F21/6245G06F16/9535G06F16/9536G06N20/00H04L63/0428
Inventor 刘丽娜
Owner BEIJING XUEZHITU NETWORK TECH
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