Model parameter training method and device based on federated learning, equipment and medium

Abstract

The invention discloses a model parameter training method and device based on federal learning, equipment and a medium. The method comprises the following steps: when a first terminal receives encrypted second data sent by a second terminal, obtaining a corresponding loss encryption value and a first gradient encryption value; randomly generating a random vector with the same dimension as the first gradient encryption value, performing fuzzy on the first gradient encryption value based on the random vector, and sending the fuzzy first gradient encryption value and the loss encryption value toa second terminal; when the decrypted first gradient value and the loss value returned by the second terminal are received, detecting whether the model to be trained is in a convergence state or not according to the decrypted loss value; and if yes, obtaining a second gradient value according to the random vector and the decrypted first gradient value, and determining the sample parameter corresponding to the second gradient value as the model parameter. According to the method, model training can be carried out only by using data of two federated parties without a trusted third party, so thatapplication limitation is avoided.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a model parameter training method, device, equipment and medium based on federated learning. Background technique [0002] "Machine learning" is one of the core research fields of artificial intelligence, and how to continue machine learning on the premise of protecting data privacy and meeting legal and compliance requirements is a trend that the field of machine learning is now paying attention to. In this context, people The research proposes the concept of "federated learning". [0003] Federated learning uses technical algorithms to encrypt the built model. Both sides of the federation can also conduct model training to obtain model parameters without giving their own data. Federated learning protects user data privacy through parameter exchange under the encryption mechanism, data and the model itself It will not be transmitted, nor can it guess the other p...

AI Technical Summary

Problems solved by technology

However, the current federated learning technology must rely on a trusted third party to aggregate and model the data of both sides of the federation through the third party, which makes the application of federated learning limited in some scenarios

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