Federated modeling method, device and equipment based on block chain, and storage medium

A modeling method and blockchain technology, applied in the field of equipment, storage media, devices, and blockchain-based federated modeling methods, can solve problems such as federated learning that is difficult to achieve gradient privacy protection

Active Publication Date: 2020-08-18
PENG CHENG LAB +1
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] The main purpose of the present invention is to provide a blockchain-based federated modeling method, device, device, and storage medium, aiming to solve th

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  • Federated modeling method, device and equipment based on block chain, and storage medium
  • Federated modeling method, device and equipment based on block chain, and storage medium
  • Federated modeling method, device and equipment based on block chain, and storage medium

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no. 1 example

[0112] Based on the first embodiment, a second embodiment of the blockchain-based federal modeling method of the present invention is proposed. In this embodiment, step S300 includes:

[0113] Step S310, the training initiator decrypts the aggregated gradient through the second key in the homomorphic encryption key to obtain the target gradient;

[0114] Step S320, the training initiator updates the model to be trained based on the target gradient, and determines whether the updated model to be trained meets a preset condition;

[0115] Step S330, if the updated model to be trained meets the preset condition, then use the updated model to be trained as the target model;

[0116] Step S340, if the updated model to be trained does not meet the preset conditions, then use the updated model to be trained as the model to be trained, and return to execute the training initiator to distribute the model to be trained to each The step of training the client.

[0117] In this embodime...

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Abstract

The invention discloses a federated modeling method, device and equipment based on a block chain, and a storage medium. The method comprises the following steps: a training initiator issues configuration information to all training clients based on client information corresponding to the training clients when the number of the training clients is monitored to reach a preset number; the training initiator uploads a to-be-trained model to a main chain; and the training initiator determines a target model based on aggregation gradient and the to-be-trained model. Modeling of federated learning isrealized through the block chain; on the premise of protecting the privacy of federated learning data, the accuracy of federated learning is not affected, the training effect and model precision of federated learning are improved, model parameters such as gradients in transmission do not need to be modified, and the balance between privacy protection of the model parameters such as gradients andmodel convergence or model precision is achieved; and information leakage can be completely prevented, so the safety of data samples in federated learning is improved.

Description

technical field [0001] The present invention relates to the technical field of federated learning, in particular to a blockchain-based federated modeling method, device, equipment and storage medium. Background technique [0002] Federated learning disassembles centralized machine learning into distributed machine learning, distributes machine learning tasks to terminal devices for learning, and then aggregates gradient results generated by machine learning to achieve the purpose of protecting the privacy of terminal devices. [0003] However, due to the unreadability of the gradient results generated by machine learning and the fact that the results may hide private information, there is a problem of privacy leakage in the federated learning mechanism. For example, in many training scenarios, the training network has hundreds or thousands of layers, and it is difficult for client users without a machine learning background to understand the specific role of each layer of th...

Claims

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Application Information

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IPC IPC(8): G06F21/60G06F21/62G06N3/04G06N3/08
CPCG06F21/602G06F21/6245G06N3/08G06N3/045
Inventor 张琰吴宇段经璞武鑫李清李伟超
Owner PENG CHENG LAB
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