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Federal learning method and device

A learning method and federated technology, applied in the field of machine learning, can solve problems such as low data security, achieve the effect of improving security and reducing the risk of data leakage

Pending Publication Date: 2022-01-11
JINGDONG TECH HLDG CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The embodiment of this application provides a federated learning method and device to solve the problem of low data security in the prior art

Method used

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  • Federal learning method and device
  • Federal learning method and device
  • Federal learning method and device

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Embodiment Construction

[0077] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments It is a part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of this application.

[0078] In the prior art, the server-client (Master-Client) structure is a commonly used federated learning framework, and the server is at the core of the entire architecture. Specifically, a single server is connected to multiple data clients, and the server manages the training of the entire federated learning, data aggregation and task assignment.

[0079...

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Abstract

The embodiment of the invention provides a federal learning method and device, applied to a virtual server in a block chain system; the virtual server being any candidate server in the block chain system, the method comprising: creating a federal learning task, the federal learning task comprising a plurality of to-be-trained algorithms; determining a first server from a plurality of candidate servers, wherein the first server is used for initiating training of a first training algorithm in the to-be-trained algorithms to a plurality of clients in the block chain system; and sending a training instruction of the first training algorithm to the first server. Compared with the prior art, due to the fact that the candidate server side used by each training algorithm is determined from the multiple candidate server sides through the virtual server side, a single server side cannot aggregate all data, the risk of data leakage is reduced, and the data safety is improved.

Description

technical field [0001] The present invention relates to the field of machine learning, in particular to a federated learning method and device. Background technique [0002] Federated learning, also known as federated machine learning (Federated machine learning / Federated Learning), is a machine learning framework. Federated learning can effectively help multiple devices perform data usage and machine learning modeling while meeting the requirements of user privacy protection, data security, and government regulations. As distributed machine learning, federated learning can effectively solve the problem of data islands, allowing participants to jointly model without sharing data, technically breaking data islands, and realizing artificial intelligence (AI) collaboration. [0003] In the prior art, the server-client (Master-Client) structure is a commonly used federated learning framework, and the server is at the core of the entire architecture. Specifically, a single serv...

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Application Information

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IPC IPC(8): G06N20/20
CPCG06N20/20H04L67/10H04L67/1044H04L63/083
Inventor 王佩琪刘展顾松庠孙海波王义
Owner JINGDONG TECH HLDG CO LTD
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