Insights into Multi-Model Federated Learning: An Advanced Approach for Air Quality Index Forecasting

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.

Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment

“…Each row represents a distinct study, highlighting the authors, the specific federated learning framework deployed, the targeted applications, the primary methods or approaches employed, and the key findings. From advanced deep learning models, like CNN and LSTM [43,84], to unique client selection strategies [85], the table shows the breadth and diversity of techniques used within the context of federated learning. It also emphasizes the impact and benefits of these methods, such as significant improvements in computational efficiency and accuracy, superior performance compared to local training, privacy preservation, and secure and efficient data collection.…”

“…LSTM networks, a subset of RNNs, also exhibit strong forecasting capabilities, particularly for PM 2.5 concentrations, AQI, and overall air pollution levels, often surpassing traditional time-series models [103,120,121]. Concurrently, ensemble and multi-model deep learning approaches present a significant enhancement in prediction accuracy [85,122,123]. For researchers venturing into this domain, it is imperative to explore CNN adaptability across varying data scales; delve deeper into LSTM's robustness, feature selection, and spatiotemporal adaptability; and rigorously investigate ensemble methodologies while concurrently addressing model interpretability.…”

“…Studies underline the potential of LSTM networks for air quality forecasting but highlight the need for further research on LSTM model robustness, the effect of feature selection, and adaptability to diverse spatiotemporal resolutions Ensemble and multi-model deep Learning [85,122,123] Air quality data Air quality forecasting…”

Thực Trạng Kiểm Soát Đường Huyết Và Một Số Yếu Tố Liên Quan Trên Bệnh Nhân Đái Tháo Đường Type 2 Tại Bệnh Viện Nội Tiết Trung Ương Năm 2022

“…Nhóm bệnh nhân có độ tuổi trên 65 chiếm tỉ lệ cao nhất là 51,7%. Kết quả này tương đồng với các nghiên cứu khác tại Việt Nam như Nguyễn Khánh Ly và cộng sự với tuổi trung bình là 64,2 ± 10,6 tuổi 36 [5], Bùi Thị Khánh thuận và cộng sự với tuổi trung bình 63 ± 10,24 tuổi [6]…”

A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs

“…Then, only the model updates (not the data) are shared with a global model, ensuring data privacy and reducing communication costs. The applications of federated learning have been extended to weather forecasting and air quality control using historical data and edge devices [1,2]. This collaborative yet decentralized learning method is crucial for weather prediction due to the inherently localized nature of weather events and the potential sensitivity of data.…”