FedDeep: A Federated Deep Learning Network for Edge Assisted Multi-Urban PM2.5 Forecasting

Abstract

Accurate urban PM2.5 forecasting serves a crucial function in air pollution warning and human health monitoring. Recently, deep learning techniques have been widely employed for urban PM2.5 forecasting. Unfortunately, two problems exist: (1) Most techniques are focused on training and prediction on a central cloud. As the number of monitoring sites grows and the data explodes, handling a large amount of data on the central cloud can cause tremendous computational pressures and increase the risk of data leakages. (2) Existing methods lack an adaptive layer to capture the varying impacts of different external factors (e.g., weather conditions, temperature, and wind speed). In this paper, a federated deep learning network (FedDeep) is developed for edge-assisted multi-urban PM2.5 forecasting. First, we assign each urban region to an edge cloud server (ECS). An external spatio-temporal network (ESTNet) is then deployed on each ECS. Data from different urban regions are uploaded to the corresponding ECS for training, which avoids processing all the data on the central cloud and effectively alleviates computational pressure and data leakage issues. Second, in ESTNet, we develop a gating fusion layer to adaptively fuse external factors to improve prediction accuracy. Finally, we adopted PM2.5 data collected from air quality monitoring sites in 13 prefecture-level cities, Jiangsu Province for validation. The experimental results proved that FedDeep outperformed the advanced baselines in terms of prediction accuracy and model efficiency.