Federated learning model training method based on data feature perception aggregation

Abstract

The invention provides a federated learning model training method based on data feature perception aggregation. According to the method, a cloud server calculates a global optimal model according to alocal model uploaded by an edge client, performs dimension reduction on data characteristics according to mined edge client data characteristics, screens out key characteristics, and performs clustering grouping on the edge client based on the key characteristics; the cloud server issues grouping information and a global optimal model to the edge client; and the edge client provides local data according to the received global optimal model and provides local execution model training according to the global optimal model, and one local model is randomly selected from the same edge client groupor the optimal model in the edge client group is selected and uploaded to the cloud server. According to the method, the data characteristics of the edge clients are fully utilized, the edge clientsare grouped, unnecessary communication is avoided, the influence of statistical isomerism on model training is relieved to a great extent, and the training efficiency of the model is improved.

Description

technical field [0001] The invention relates to the field of edge computing, in particular to a federated learning model training method based on data feature perception aggregation. Background technique [0002] Since cloud computing was proposed in 2005, it has gradually changed the way we live, study and work. Services provided by software such as Google and Facebook that are often used in daily life are typical representatives. Moreover, scalable infrastructure and processing engines that can support cloud services have also had a certain impact on our business model, such as Hadoop, Spark, and so on. The rapid development of the Internet of Things has brought us into the post-cloud era. In our daily life, a large amount of data will be generated so that the cloud computing model can no longer solve the current problems well. Therefore, a new computing model has emerged, the edge calculate. Edge computing refers to processing data at the edge of the network, which can...

AI Technical Summary

Problems solved by technology

[0005] Using the federated learning method in the edge computing scenario will amplify the existing statistical heterogeneity. When the statistical heterogeneity is large enough, the trained model effect will not meet the required effect.

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