Federal learning-based model training method and related equipment

A model training and federation technology, applied in the computer field, can solve problems such as input data injection attacks, achieve the effect of preventing data leakage and improving the defense against attacks

Pending Publication Date: 2021-12-14
STATE GRID ZHEJIANG ELECTRIC POWER +1
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Insufficient validation of input data can lead to malicious injection attacks

Method used

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  • Federal learning-based model training method and related equipment
  • Federal learning-based model training method and related equipment
  • Federal learning-based model training method and related equipment

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Embodiment Construction

[0042] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0043] The model training method based on federated learning provided by the embodiments of the present disclosure is applied to an edge computing framework. The edge computing framework includes a cloud computing center and at least one edge computing node. The cloud computing center manages and controls each edge computing node.

[0044] Among them, Federated Learning (Federated Learning) is an emerging basic technology of artific...

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Abstract

The invention provides a federated learning-based model training method and related equipment. The method and the equipment are applied to an edge computing framework, the edge computing framework comprises a cloud computing center and at least one edge computing node, and the cloud computing center manages and controls each edge computing node. The method comprises the following steps: constructing a central neural network model for the cloud computing center; constructing an edge neural network model for each edge computing node; and carrying out federated learning on each edge neural network model by using a training sample, and adjusting the central neural network model to obtain an anti-attack defense global model. Federated learning is carried out between the edge neural network models corresponding to the edge computing nodes, so that data leakage of the edge computing nodes can be effectively prevented in the training process, and the attack defense capability of the edge computing framework is improved.

Description

technical field [0001] The present disclosure relates to the field of computer technology, and in particular to a federated learning-based model training method and related equipment. Background technique [0002] Edge computing is a new computing paradigm that is considered to perform computing at the edge of the network after distributed computing, grid computing, and cloud computing. Its core idea is to bring computing closer to the source of data. [0003] Security issues that occur under edge computing. Insufficient validation of input data can lead to malicious injection attacks. This malicious data injection is a common attack method for edge computing. Two types of attacks can be launched against machine learning methods used in IoT systems: causal attacks and exploratory attacks. In a causal attack, the attacker alters the training process by manipulating the training dataset, while in an exploratory attack, the attacker exploits vulnerabilities to gain informati...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N20/00G06N3/04G06N3/08
CPCG06N20/00G06N3/04G06N3/08
Inventor 王红凯冯珺潘司晨江樱琚小明黄海潮张烨华彭梁英赵帅张朋飞陈可谢裕清刘瀚琳
Owner STATE GRID ZHEJIANG ELECTRIC POWER
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