Federation learning method and device based on evolutionary computation, central server and medium

Abstract

The invention discloses a federated learning method and device based on evolutionary computation, a central server and a medium. The method comprises the following steps of: grouping the participantsparticipating in federated learning based on a grouping strategy; obtaining hyper-parameter sets, respectively sending each hyper-parameter in the hyper-parameter set to participants in different groups, so that participants in different groups can perform federated learning based on the received hyper-parameters; obtaining a sub-target model corresponding to each group; performing iterative evolution calculation based on the performance index, the grouping strategy and the hyper-parameter set of each sub-target model; and finally, sending the target hyper-parameter to each participant participating in federated learning, so that the participant determines respective initial model parameters based on the target hyper-parameter, and each participant performs federated learning based on theinitial model parameters to obtain a target model. The hyper-parameter optimization efficiency in federated learning is improved through evolutionary computation, and meanwhile, the performance of a federated learning model is remarkably improved.

Description

technical field [0001] The present invention relates to the field of financial technology, in particular to a federated learning method, device, central server and readable storage medium based on evolutionary computation. Background technique [0002] In federated learning, complex models such as neural networks are often used. This type of model has a large number of hyperparameters, such as learning rate, number of network layers, and the dimension of each convolution kernel. The existing federated learning algorithm can only train the neural network on the set hyperparameters. Since the artificially set fixed hyperparameters are often not the optimal hyperparameters, the federated learning model trained under the artificially given hyperparameters often cannot achieve the best results. In order to obtain a good federated learning model, it is necessary to continuously adjust the hyperparameters artificially based on experience, and to perform federated learning again. ...

AI Technical Summary

Problems solved by technology

[0004] The main purpose of the present invention is to provide a federated learning method, device, central server, and readable storage medium based on evolutionary computing, aiming to solve the problem of low efficiency in determining hyperparameters in existing federated learning and affecting federated learning performance indicators question

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