Trusted federated learning method, system and device based on block chain and medium

A learning method and blockchain technology, applied in the field of trusted federated learning, can solve problems such as low security, destruction of model privacy data, unfair system, etc., and achieve the effect of improving security
CN111966698AActive Publication Date: 2020-11-20SOUTH CHINA NORMAL UNIVERSITY
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
SOUTH CHINA NORMAL UNIVERSITY
Publication Date
2020-11-20

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Abstract

The invention discloses a trusted federation learning method, system and device based on a block chain and a medium, and the method comprises the steps: selecting a client node from the block chain toform an initial committee, and determining an initial shared global model; training the initial shared global model through each client node in a block chain to obtain local model updating information of each client node; generating a target global model by the initial committee according to the local model updating information of each client node; and determining a target committee through the dynamic multi-weight reputation model, and starting a new round of training until a target global model meeting convergence requirements is obtained. According to the invention, the central server is removed by using the blockchain technology, so that the distributed client nodes are stored dispersedly, the security of private data is improved, and the method, system and device can be widely applied to the technical field of blockchains.
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Description

technical field

[0001] The present invention relates to the technical field of blockchain, in particular to a trusted federated learning method, system, device and medium based on blockchain. Background technique

[0002] With the rapid development of artificial intelligence (AI), various mobile phone applications have brought excellent customer experience to mobile users. However, most AI technologies require a large amount of user data and personal privacy information for model training on a central server, resulting in too much calculation that is not suitable for mobile devices. In addition, mobile devices also face serious risks of privacy leakage.

[0003] Google first proposed federated learning to solve the privacy problem under collaborative computing. Traditional federated learning consists of participating devices and a central server. Participating devices do not upload private data but only iteratively send local model updates to the central server. The centra...

Claims

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