Air Quality Decentralized Forecasting: Integrating IoT and Federated Learning for Enhanced Urban Environmental Monitoring

Abstract

Air quality forecasting is a critical environmental challenge with significant implications for public health and urban planning. Conventional machine learning models, although quite effective, require data collection, which can be hampered by issues relating to privacy and data security. Federated Learning (FL) overcomes these limitations by enabling model training across decentralized data sources without compromising data privacy. This study describes a federated learning approach to predict the Air Quality Index (AQI) based on data from several Internet of Things (IoT) sensors located in different urban locations. The proposed approach trains a model using data from different sensors while preserving the privacy of each data source. The model uses local computational resources at the sensor level during the initial data processing and training, sharing only the model updates to the central location. The results show that the performance of the proposed FL model is comparable to a centralized model and ensures better data privacy with reduced data transmission requirements. This study opens new doors to real-time, scalable, and efficient air quality monitoring systems. The proposed method is quite significant for smart city initiatives and environmental monitoring, as it provides a solid framework for using IoT technology while preserving privacy.