Enhancing trash classification in smart cities using federated deep learning.

Autor: Ahmed Khan H; Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, 45550, Pakistan., Naqvi SS; Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, 45550, Pakistan., Alharbi AAK; Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia., Alotaibi S; Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia., Alkhathami M; Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia. maalkhathami@imamu.edu.sa.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 May 23; Vol. 14 (1), pp. 11816. Date of Electronic Publication: 2024 May 23.
DOI: 10.1038/s41598-024-62003-4
Abstrakt: Efficient Waste management plays a crucial role to ensure clean and green environment in the smart cities. This study investigates the critical role of efficient trash classification in achieving sustainable solid waste management within smart city environments. We conduct a comparative analysis of various trash classification methods utilizing deep learning models built on convolutional neural networks (CNNs). Leveraging the PyTorch open-source framework and the TrashBox dataset, we perform experiments involving ten unique deep neural network models. Our approach aims to maximize training accuracy. Through extensive experimentation, we observe the consistent superiority of the ResNext-101 model compared to others, achieving exceptional training, validation, and test accuracies. These findings illuminate the potential of CNN-based techniques in significantly advancing trash classification for optimized solid waste management within smart city initiatives. Lastly, this study presents a distributed framework based on federated learning that can be used to optimize the performance of a combination of CNN models for trash detection.
(© 2024. The Author(s).)
Databáze: MEDLINE
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