Smart City Gnosys

Smart city article details

Title	Insights Into Multi-Model Federated Learning: An Advanced Approach For Air Quality Index Forecasting
ID_Doc	31767
Authors	Le D.-D.; Tran A.-K.; Dao M.-S.; Nguyen-Ly K.-C.; Le H.-S.; Nguyen-Thi X.-D.; Pham T.-Q.; Nguyen V.-L.; Nguyen-Thi B.-Y.
Year	2022
Published	Algorithms, 15, 11
DOI	http://dx.doi.org/10.3390/a15110434
Abstract	The air quality index (AQI) 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 (ML) 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 (FL) 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. In this survey, we went over the works that previous scholars have conducted in AQI forecast both in traditional ML approaches and FL mechanisms. Our objective is to comprehend previous research on AQI prediction including methods, models, data sources, achievements, challenges, and solutions applied in the past. We also convey a new path of using multi-model FL, which has piqued the computer science community’s interest recently. © 2022 by the authors.
Author Keywords	AQI prediction; big data; federated learning; IoT; multi-model

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