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Title	Fed-Rest: Federated Learning-Based Recurrent Long Short-Term Memory Model For Smart Cities Air Quality Prediction
ID_Doc	26309
Authors	Chatterjee K.; Gurujyothi S.; Kumar B.K.; Selvamuthukumaran N.; Suraj S.; Reddy M.S.; Sachin A.; Thara M.N.
Year	2024
Published	Lecture Notes in Networks and Systems, 1045 LNNS
DOI	http://dx.doi.org/10.1007/978-981-97-4799-3_29
Abstract	The proliferation of IoT has garnered widespread attention due to its versatile applications across manufacturing, commerce, and education industries. Within this context, the smart city (SC) framework is based. However, this SC framework generates an environmental issue: air pollution. Therefore, to address this challenge, we aim to design an accurate air quality predictor for SCs, namely Fed-ReST. To achieve this goal, we implement a Recurrent Long Short-Term Memory (RLSTM) network inside the Federated Learning (FL) environment. With this architecture, we can handle spatiotemporal correlation among the participating air contaminants (ACs) with each other and with meteorological factors (MFs). The used RLSTM network is responsible for accurately forecasting future SC’s air quality, while FL is responsible for the model’s secure, decentralized, and distributed training. For precise evaluation, we use Pearson Correlation Coefficient (PLCC) and Root Mean Square Error (RMSE) metrics. Our proposed Fed-ReST model performs better than 62, 47, 37, 27, and 14% as compared with Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Stacked Long Short-Term Memory (SLSTM), Gated Recurrent Unit (GRU), and Bi-directional Gated Recurrent Unit (BGRU) models, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Author Keywords	Deep Learning (DL); Federated Learning (FL); Machine Learning (ML); Smart City (SC); Spatiotemporal

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