High-quality federal learning system and learning method based on block chain and reputation mechanism

A learning system and learning method technology, applied in the field of high-quality federated learning systems, can solve problems such as low quality, difficult to measure data quality, high risk of single point failure, etc., to achieve the effect of promoting enthusiasm and reducing impact

Inactive Publication Date: 2022-03-08
ZHEJIANG NORMAL UNIVERSITY
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  • Application Information

AI Technical Summary

Problems solved by technology

[0005] However, the existing technology does not have a method of building a federated learning system based on blockchain technology, and there is no federated learning system based on blockchain
[0006] Through the above analysis, the existing problems and defects of the existing technology are: the existing technology does not have a method of building a federated learning system based on blockchain technology, nor does it have a federated learning system based on ...

Method used

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  • High-quality federal learning system and learning method based on block chain and reputation mechanism
  • High-quality federal learning system and learning method based on block chain and reputation mechanism
  • High-quality federal learning system and learning method based on block chain and reputation mechanism

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Experimental program
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Embodiment 1

[0118] 1 Blockchain-based federated learning system

[0119] 1.1 Role Definition

[0120] Data owner: The data owner holds private data that can be used for model training, and these data are not directly shared externally. Such nodes use private data to train models locally and share data externally by sharing local models.

[0121] Model Aggregators: Model Aggregators hold private data that can be used for model evaluation. The collected local models will be shared among the model aggregators, and the model evaluation will evaluate the reputation of the collected local models. Model aggregators eventually collaborate to generate a global evaluation of the model.

[0122] 1.2 System Architecture

[0123] Such as diagram 2-1 The system shown is mainly composed of three layers: blockchain layer, reputation layer, and model training layer.

[0124] The blockchain layer is the foundation of the entire system. At this layer, data owners and model aggregators share local mo...

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Abstract

The invention belongs to the technical field of federal learning, and discloses a high-quality federal learning system and learning method based on a block chain and a reputation mechanism, and a block chain layer shares a global model and reputation evaluation by taking an intelligent contract as a carrier; the reputation layer scores the local models shared by all the data owners by using a model scoring mechanism; reputation evaluation is carried out on each data owner by using a reputation mechanism; a model training layer data owner carries out training of a local model and shares the local model to a model aggregator; and meanwhile, the model aggregator aggregates the collected models in combination with the reputation and awards participating data owners. The federated learning system based on the block chain disclosed by the invention has important guidance and technical significance for realizing a high-quality model aggregation task, is beneficial to improving the security and robustness of federated learning, and is beneficial to promoting the fairness of reward distribution and the enthusiasm of participating in the task.

Description

technical field [0001] The invention belongs to the technical field of federated learning, and in particular relates to a high-quality federated learning system and learning method based on blockchain and reputation mechanism. Background technique [0002] Currently, federated learning is a machine learning paradigm proposed by Google to solve privacy and security issues in machine learning. Federated learning no longer uses centrally stored data to train models, but requires participants to use their own data to perform model training locally and send model-related parameters or gradients to the central aggregation server. The Model Central Server uses these local models to generate an accurate global model. During this whole process, since the personal data of the participants does not need to be directly involved in sharing, the security of personal data is well protected while obtaining the ideal model. [0003] Federated learning faces a key problem: how to encourage ...

Claims

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Application Information

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IPC IPC(8): G06N20/20G06F21/64G06Q30/02
CPCG06N20/20G06Q30/0208G06F21/64
Inventor 林飞龙齐嘉浩陈中育王晓虎郑忠龙
Owner ZHEJIANG NORMAL UNIVERSITY
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