The Design And Implementation Of Air Quality Prediction System Based On Federated Transfer Learning

In recent years,with the rapid development of the urban economy,air pollution has become a major problem in the urban environment.To prevent and control air pollution,it is necessary to monitor and accurately predict the air quality of cities.However,the high cost of air quality monitoring,the maintaing difficulty and the unbalance development of each city lead to regional limitations in the observation data of monitoring stations.Based on the above reasons,this paper is about designing and implementing a new type of air quality prediction system based on Federated Transfer Learning(FTL).Based on the basic layer,data layer,service layer and application layer,the design process of the whole air quality prediction system is described in detail.In particular,regional matching(Regions-Match),source city region training(S-Train),region-to-migration(Regions-Tran)and a target city region Federated Transfer Learning(T-FRL)are designed to ensure the accuracy and efficiency of system prediction.Furthermore,the entire system has been implemented based on the Python and the TensorFlow platform.And we test system stability and performance indicators.The test results show that compared with using only Federated learning or transfer learning,federal transfer learning improves the accuracy by 6.07%and 12.83%respectively.