Federation learning model training method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a federated learning model training method and device, equipment and a storage medium, and belongs to the technical field of machine learning. The method comprises the following steps: generating an ith scalar operator based on an ith model parameter of an ith sub-model and an ith gradient; sending an ith fusion operator to the next node device based on the ith scalar operator; based on the obtained second-order gradient scalar, the ith model parameter and the ith first-order gradient, determining an ith second-order gradient descending direction of the ith sub-model; and updating the ith sub-model based on the ith second-order gradient descending direction. In the embodiment of the invention, the model iterative training is completed by transmitting the fusion operator between the node devices and jointly calculating the second-order gradient descent direction of the sub-model, and the machine learning model can be trained by using the second-order gradient descent method without depending on a third-party node, so that the problem of security risk in single-point concentration can be avoided, the safety of federated learning is enhanced, and practical application is facilitated.

Description

technical field [0001] The embodiments of the present application relate to the technical field of machine learning, and in particular to a federated learning model training method, device, device, and storage medium. Background technique [0002] Federated machine learning is a machine learning framework that can combine data sources from multiple parties to train machine learning models while ensuring that the data does not leave the domain, so that on the basis of meeting privacy protection and data security, multi-party data sources can be used to improve Model performance. [0003] In related technologies, the model training phase of federated learning requires a trusted third party as the central coordinating node to send the initial model to each participant, and collect the models trained by each participant using local data, so as to coordinate the aggregation of the models of all parties , and then send the aggregated model to each participant for iterative traini...

AI Technical Summary

Problems solved by technology

[0004] However, relying on a third party for model training enables the third party to obtain the model parameters of all other participants, but there is still the problem of leaking private data, the security of model training is low, and it is difficult to find a trusted third party. High, making it difficult to apply the solution

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