Spatially-distributed Federated Learning of Convolutional Recurrent Neural Networks for Air Pollution Prediction

Air pollution prediction for smart city applications has been attracted in artificial intelligence research to overcome problems to the health of citizen. Conventionally, environmental IoT data is gathered from monitoring station sensors then is sent to servers for centralized predictive model training at a whole region. That causes latency issues in data transmission from IoT devices to cloud servers. This paper describes federated learning paradigm approach for air pollution prediction model training on environmental monitoring sensor data. In the research, we design distributed learning framework that assists cooperative training among participants from different spatial areas such as cities and prefectures. At each area, Convolutional Recurrent Neural Networks (CRNN) are trained locally aiming to predict local Oxidant warning level while aggregated global model enhances distilled knowledge from all areas of a region. The research illustrates that designed common parts of CRNN can be fused globally meanwhile adaptive structure at predictive part of the deep neural network model can capture different environmental monitoring stations configuration at local areas. Some experiment results also hint methods to keep balance between federated learning synchronous training rounds and local deep neural network training epochs to maximize accuracy of the whole federated learning system. The results also prove that new participating areas can train and quickly obtain optimized local models by using transferred common global model.