| Title | Enhancing trash classification in smart cities using federated deep learning |
|---|---|
| ID_Doc | 44275 |
| Authors | Khan, HA; Naqvi, SS; Alharbi, AAK; Alotaibi, S; Alkhathami, M |
| Title | Enhancing trash classification in smart cities using federated deep learning |
| Year | 2024 |
| Published | Scientific Reports, 14, 1 |
| Abstract | 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. |
| https://www.nature.com/articles/s41598-024-62003-4.pdf |
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