Federated learning method and device based on evolutionary computation, equipment and medium

Abstract

The invention discloses a federated learning method and devicebased on evolutionary computation, equipment and a readable storage medium. The method comprises the following steps of: determining hyper-parameters of a first group corresponding to the first equipment in a hyper-parameter set, whereinthe equipment in the first group performs federated learning based on the hyper-parameters of the first group; obtaining a first sub-target model corresponding to the first group, receiving performance indexes of a second sub-target model sent by a plurality of pieces of second equipment, performingiterative evolution calculation based on the performance indexes of the first sub-target model, the performance indexes of the plurality of second sub-target models and the hyper-parameter set, wherein the first equipment and the plurality of second equipment determine respective initial model parameters based on the target hyper-parameters, and the first equipment and the plurality of second equipment perform federated learning based on the respective initial model parameters to obtain a target model. The performance of the federated learning model is remarkably improved while the hyper-parameter optimization efficiency in federated learning is improved.

Description

technical field [0001] The present invention relates to the field of financial technology, in particular to a federated learning method, device, equipment 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. The w...

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Problems solved by technology

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

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