A wireless traffic forecasting method based on weighted federated learning

Abstract

The invention relates to a wireless service traffic prediction method based on weighted federated learning. The method comprises: a control center pushes multiple pre-trained models to the base station side; the base station side performs model training according to local data, and transmits the trained models to Control center; the control center fuses the models according to the weighting rules and feeds them back to the base station. The weighting rules give more weight to the local model; the base station predicts the wireless service traffic in the future according to the obtained final model. The wireless service flow prediction method provided by the present invention uses weighted aggregation rules to replace the average strategy in the model aggregation strategy of the control center, which can avoid inaccurate predictions caused by data heterogeneity and improve distributed wireless service flow prediction. overall prediction accuracy.

Description

technical field [0001] The invention relates to a wireless service flow prediction method based on weighted federated learning, which belongs to the technical fields of communication network and artificial intelligence. Background technique [0002] The traditional centralized wireless service traffic forecasting method needs to collect large-scale service data scattered in different nodes to the central node, and then centrally process, train and predict these data. Subsequently, according to the prediction result, the core network dynamically adjusts the base station through the control unit, such as increasing or decreasing the number of baseband processing units to adjust the service capability of the base station. [0003] However, due to the limited bandwidth of data transmission and data privacy issues, it takes a lot of resources to transmit data to the cloud center, resulting in network congestion; in addition, as users' requirements for data privacy protection cont...

AI Technical Summary

Problems solved by technology

[0005] However, in the traditional federated learning algorithm, only the average operation is performed on the model, ignoring the differences between the models

Due to the different locations of the base stations and the different mobile and communication behaviors of users within the coverage area, this leads to large data differences. The traditional simple average cannot accurately capture the traffic patterns of different base stations, so the prediction effect is not accurate.

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