Hierarchical federated learning method and device based on asynchronous communication, terminal equipment and storage medium

Abstract

The invention provides a hierarchical federated learning method and device based on asynchronous communication, terminal equipment and a storage medium, and relates to the technical field of wirelesscommunication networks. The method comprises the following steps: an edge server issues a global model to an intra-cluster client to which the global model belongs; the client updates the model by using the local data and uploads the model to each belonging cluster edge server; the edge server determines to update the clients in the cluster according to the client update uploading time; the received model parameters are averaged by the edge server, and it is selected to asynchronously upload the model parameters to the central server or directly issue the model parameters to the client according to the updating times of the current client; and the central server performs weighted averaging on the parameters uploaded by the edge server, and issues the parameters to the client for training until the local model converges or reaches an expected standard. According to the method, the federated learning task can be efficiently executed, the communication cost required by the parameters of the federated learning model is reduced, the edge server butted with the client is dynamically selected, and the overall training efficiency of the federated learning is improved.

Description

technical field [0001] The present invention relates to the technical field of wireless communication networks, in particular to a layered federated learning method, device, terminal equipment and storage medium based on asynchronous communication. Background technique [0002] In recent years, with the rise of technologies such as the Internet of Things, mobile communications, and wearable devices, the amount of data in the network has grown explosively, which directly promotes the rapid development of data-driven machine learning technology (especially deep learning). However, several recent information leakage incidents have brought people's attention back to data security. Most of the information collected by mobile phones and wearable devices is related to personal privacy. As people's awareness of privacy protection continues to increase, users are often unwilling to share their information with institutions, organizations or others, resulting in the traditional collec...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a layered federated learning method, device, terminal device and storage medium based on asynchronous communication, which can solve the heterogeneity problem of the federated learning system, and can significantly improve the efficiency of model training; solve the problem of synchronous iteration and communication efficiency. Make better use of the strengths of federated learning and apply federated learning to more practical scenarios

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