Wireless service flow prediction method based on weighted federated learning

Abstract

The invention relates to a wireless service flow prediction method based on weighted federated learning. The method comprises the following steps: a control center pushes a plurality of pre-training models to a base station side; the base station side performs model training according to the local data and transmits the trained model to a control center; the control center fuses the models according to a weighting rule and feeds back the fused models to the base station, and more weights are given to the local model in the weighting rule; and the base station predicts the wireless service flowat the future moment according to the obtained final model. According to the wireless service flow prediction method provided by the invention, on the basis of a control center model aggregation strategy, the weighted aggregation rule is used for replacing an average strategy, so that the phenomenon of inaccurate prediction caused by data isomerism can be avoided, and the overall prediction precision of distributed wireless service flow prediction is improved.

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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