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

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...

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 the problem of difficulty in achieving gradient privacy protection and model convergence or model accuracy in existing federated learning. balanced technical issues

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