Federation learning method and device, computer equipment and medium

Abstract

The embodiment of the invention discloses a federated learning method and device, computer equipment and a medium, and belongs to the technical field of computers. The method comprises the following steps that: first computer equipment acquires sample label information corresponding to a sample identifier, and acquires first fusion information corresponding to the sample identifier; the second computer equipment acquires second fusion information corresponding to the sample identifier and sends the second fusion information to the first computer equipment; the first computer equipment acquiresa gradient operator corresponding to the sample identifier based on the first fusion information, the second fusion information and the sample label information, and sends the gradient operator to the second computer equipment; and the first computer equipment and the second computer equipment respectively adjust model parameters of a first sub-model and model parameters of a second sub-model inthe machine learning model based on the gradient operator. According to the method, the training speed of the model is increased while the privacy of the user is ensured, the information amount of thecharacteristics of the sample is enriched, and the accuracy of the model is improved.

Description

technical field [0001] The embodiments of the present application relate to the field of computer technology, and in particular to a federated learning method, device, computer equipment and media. Background technique [0002] With the development of computer technology and the advancement of artificial intelligence (AI) technology, federated learning has gradually become a hot topic in the field of AI. Federated learning completes the training of neural network models through multi-party cooperation, protecting user privacy. At the same time as data security, it solves the problem of data islands. [0003] Because the characteristics of the samples included in different sample sets are different, and the label information corresponding to the samples in different sample sets is not exactly the same, for example, the first sample set includes the user's bank transfer records and labels indicating the user's risk level, and the second The second sample set includes the user...

AI Technical Summary

Problems solved by technology

However, horizontal federated learning, vertical federated learning, or federated transfer learning are not suitable for this scenario. Therefore, a new federated learning method is urgently needed to process samples in different sample sets.

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