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Title	Federated Learning For Air Quality Index Prediction: An Overview
ID_Doc	26335
Authors	Le D.-D.; Tran A.-K.; Dao M.-S.; Nazmudeen M.S.H.; Mai V.-T.; Su N.-H.
Year	2022
Published	Proceedings - International Conference on Knowledge and Systems Engineering, KSE, 2022-October
DOI	http://dx.doi.org/10.1109/KSE56063.2022.9953790
Abstract	The air quality index forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and review papers using machine learning for air quality analysis have been published. However, most of those studies focused on traditional centralized processing on a single machine, and there had been few surveys of federated learning in this field. This overview aims to fill this gap and provide newcomers with a broader perspective to inform future research on this topic, especially for the multi-model approach. We have examined over 70 carefully selected papers in this scope and discovered that multi-model federated learning is the most effective technique that could enhance the air quality index prediction result. Therefore, this mechanism needs to be considered by science community in the coming years. © 2022 IEEE.
Author Keywords	AQI prediction; big data; federated learning; IoT; multi-model

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