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

Abstract

The invention discloses a federated learning method and device based on evolutionary computation, a central server and a readable storage medium. The method comprises the following steps of: acquiringa hyper-parameter of each participant group, and sending the hyper-parameters to each participant of each participant group to enable the participants in each participant group to carry out federatedlearning; acquiring sub-target models obtained by trained through federated learning of the participant groups, and performing iterative evolutionary computation based on the performance index of each sub-target model and the hyper-parameter corresponding to each participant group to obtain a target hyper-parameter; and sending the target hyper-parameter to each participant participating in federated learning to enable the participants to determine respective initial model parameters based on the target hyper-parameter and perform federated learning based on the initial 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 can only train the neural network on the set fixed 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 ...

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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 of optimizing hyperparameters in existing federated learning and affecting the performance of federated learning models question

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