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This study is based on federated learning technology and designs and implements a system for air quality prediction. The system consists of multiple regional terminals, each responsible for locally training air quality data within the region and generating regional models. By exchanging and integrating models between regions, global model updates and air quality predictions can be achieved. After testing, The system has achieved significant results in improving model training efficiency and prediction accuracy. This study provides a new approach and method for the development of air pollution monitoring systems. The system was developed using Python as the development tool and TensorFlow as the development platform.

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Design and Implementation of Air Quality Prediction System Based on Federated Learning

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  • 发表日期：

    2025年

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  • Yuechun Feng

摘要

This study is based on federated learning technology and designs and implements a system for air quality prediction. The system consists of multiple regional terminals, each responsible for locally training air quality data within the region and generating regional models. By exchanging and integrating models between regions, global model updates and air quality predictions can be achieved. After testing, The system has achieved significant results in improving model training efficiency and prediction accuracy. This study provides a new approach and method for the development of air pollution monitoring systems. The system was developed using Python as the development tool and TensorFlow as the development platform.