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Title	Distributed Framework For Pothole Detection And Monitoring Using Federated Learning
ID_Doc	20645
Authors	Ezil Sam Leni A.; Shalen S.
Year	2024
Published	2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024 - Proceedings
DOI	http://dx.doi.org/10.1109/ICAIT61638.2024.10690502
Abstract	Transport service monitoring and upkeep are essential components of smart city initiatives. In India, the economy is greatly impacted by the road transport sector. In 2021, the Ministry of Road Transport and Highways Transport, Government of India, produced a report with statistical data on traffic accidents. This study proposes a distributed infrastructure for the monitoring, detection, and reporting of potholes to the appropriate authorities. In a distributed environment, the nodes are the edge devices, and local edge servers, and global servers. The edge devices receive the initial model to be employed from the global server. The YOLOv8 model for pothole detection is used in the edge devices. The edge devices run the pothole detection model, gather the pothole images on their path, and send the updates to the nearby edge server. The local edge server selects the clients for its aggregation process, aggregates the model updates and sends the updates to the global server. The global server collects the updates from the local edge servers, performs aggregation and derives the updated model. The updated model has the information about the potholes received from the local edge servers and notifies the updates to the local edge servers and concerned authorities for monitoring and maintenance of road conditions. The entire process is implemented in FedCV distributed environment with the implementation using client-server model and aggregation entities. © 2024 IEEE.
Author Keywords	distributed framework; federated averaging; Federated Learning; pothole detection

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