Edge end bias detection method in federated machine learning environment

A technology of machine learning and detection methods, applied in the field of machine learning, can solve problems such as poor accuracy performance and high precision loss of a single training model, and achieve the effect of ensuring fairness

Pending Publication Date: 2021-01-05
ZHEJIANG UNIV OF TECH
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Problems solved by technology

Existing debiasing methods are mainly aimed at a single training model. This type of machine learning model usually does not perform global parameter updates. Compared with the federated machine learning model, under the same parameters, the accuracy performanc

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  • Edge end bias detection method in federated machine learning environment

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Embodiment Construction

[0031] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, and do not limit the protection scope of the present invention.

[0032] In order to solve the problem of bias in the edge model in the federated machine learning environment, the prediction results of the model have unrealistic bias and violate fairness. This embodiment provides a method for bias detection at the edge based on a federated machine learning environment, such as figure 1 As shown, the method of edge bias detection based on federated machine learning environment includes the following steps:

[0033] Step 1, construct the original dataset.

[0034]In the present invention, when the machine learning model makes predictio...

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Abstract

The invention discloses an edge end bias detection method in a federated machine learning environment. The method comprises the following steps of: screening to obtain ambiguous instances, increasingthe proportion of the ambiguous instances in a data set to construct a new data set, carrying out distributed training on a model by utilizing the new data set, obtaining the prejudice degree of eachmodel for sensitive attributes (prejudice information), removing prejudice by endowing each model with different attention weights according to the prejudice degree, and performing model aggregation after the prejudice is eliminated, thereby ensuring the fairness of an edge end in a federated machine learning environment.

Description

technical field [0001] The present invention relates to the field of machine learning, in particular to a method for edge bias detection in a federated machine learning environment. Background technique [0002] With the rise of IoT technology and edge computing, data is often not constrained to a single whole, but is distributed in many ways and exists in the form of isolated islands. The traditional data processing workflow is generally that one party collects data, then transmits it to the other party for data preprocessing and modeling, and finally generates a model and distributes it to a third party. In recent years, with the application of artificial intelligence in various industries, people's attention to user privacy and data security is gradually increasing. Users are beginning to pay more attention to whether their personal data is collected with personal permission. At the same time, with the gradual improvement and implementation of various laws and regulatio...

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Application Information

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IPC IPC(8): G06K9/62G06N20/00
CPCG06N20/00G06F18/22G06F18/23213G06F18/214
Inventor 陈晋音陈一鸣郑海斌陈奕芃
Owner ZHEJIANG UNIV OF TECH
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