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Smart city article details

Title	Leveraging Federated Learning For Weather Classification In The Era Of Smart Cities
ID_Doc	35079
Authors	Gargees R.S.
Year	2024
Published	2024 IEEE 3rd International Conference on Computing and Machine Intelligence, ICMI 2024 - Proceedings
DOI	http://dx.doi.org/10.1109/ICMI60790.2024.10585807
Abstract	The advent of smart cities has led to a significant increase in the collection and utilization of diverse data streams, including imagery datasets capturing various aspects of urban environments. This paper presents a novel approach to weather classification in the era of smart cities through the integration of federated learning (FL) techniques. This study aims to leverage decentralized computing to enhance the accuracy and efficiency of weather classification models. The federated model exhibits robustness and adaptability to the dynamic and heterogeneous nature of smart city environments. The findings of this research contribute to the advancement of weather monitoring and prediction capabilities in smart cities, fostering a more resilient and responsive urban infrastructure. Additionally, the federated learning approach demonstrated its effectiveness in managing decentralized datasets, ensuring the model training while maintaining data privacy in the context of smart city deployments. The study leverages the pre-trained ResNet50 architecture to achieve a commendable training accuracy rate of 87% and a validation accuracy rate of 86%. The implications of this research extend beyond weather classification, laying the groundwork for the broader integration of federated learning techniques in diverse applications within smart city ecosystems.
Author Keywords	federated learning; imagery data; smart cities; urban infrastructure; weather classification

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