Smart City Gnosys

Smart city article details

Title	Distributed Framework For Pothole Detection And Monitoring Using Federated Learning: A Privacy-Preserving Edge Computing Approach
ID_Doc	20646
Authors	Thamizharasi M.; Sethuraman R.; Sandhya A.
Year	2025
Published	National Academy Science Letters
DOI	http://dx.doi.org/10.1007/s40009-025-01678-3
Abstract	The research focuses on tackling challenges in increasing pothole leading to deteriorating road conditions. In reality, object detection faces various computational challenges at edge devices that includes model training, global server aggregation and data privacy. By leveraging federated learning within the FedCV framework, the system monitors, detects, and reports potholes. The novel aspect of research is the adaptation of the YOLOv8 object detection model, integrated with the FedCV framework, to ensure data privacy and security at the central server, while simultaneously boosting performance at the edge device during pothole detection training with local data. This integration addresses major computational challenges at the edge camera device level. As the research combines deep learning and federated learning within an edge-based framework, it improves real-time pothole detection and ensures efficient privacy-preserving road maintenance and advancing smart city initiatives. The model has demonstrated outstanding performance in classification, achieving an average accuracy of 96%. With a precision of 96.4%, recall of 96.7%, and an F1 score of 96.6%, its evaluation underscores its superiority compared to previous studies. These results establish the model as a reliable approach for pothole detection. © The Author(s), under exclusive licence to The National Academy of Sciences, India 2025.
Author Keywords	Aggregation; Data privacy; Distributed framework; Federated averaging; Federated learning; Performance indicator; Pothole detection

Similar Articles

Id	Similarity	Authors	Title	Published
20645	0.942	Ezil Sam Leni A.; Shalen S.	Distributed Framework For Pothole Detection And Monitoring Using Federated Learning	2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024 - Proceedings (2024)
44425	0.903	Bhosale S.B.; Ponnusamy S.	Real-Time Pothole Detection Using Yolov7: An Efficient Deep Learning Approach For Road Safety And Maintenance	2025 International Conference on Data Science and Business Systems, ICDSBS 2025 (2025)
58038	0.903	Mehta S.; Singh A.	Towards Autonomous Road Maintenance: Cnn-Fl Integration For Pothole Detection Systems	2024 4th Asian Conference on Innovation in Technology, ASIANCON 2024 (2024)
42511	0.9	Sai K.K.; Kumar D.D.V.; Sahrudhay A.; Dharavath K.	Pothole Detection Using Deep Learning	2023 2nd International Conference on Futuristic Technologies, INCOFT 2023 (2023)
204	0.894	Alshammari S.; Song S.	3Pod: Federated Learning-Based 3 Dimensional Pothole Detection For Smart Transportation	ISC2 2022 - 8th IEEE International Smart Cities Conference (2022)
16106	0.877	Lakshminarayanan S.; Konidhala J.	Convolutional Neural Network For Pothole Identification In Urban Roads	International Journal Of Advances In Signal And Image Sciences, 10, 1 (2024)
45593	0.875	Liu J.; Wang H.; Wei J.; Nan J.	Research On Road Pothole Recognition Based On Deep Convolutional Neural Networks	2024 IEEE 2nd International Conference on Image Processing and Computer Applications, ICIPCA 2024 (2024)
23667	0.872	Dwivedi S.K.; Arya D.; Sekimoto Y.	Enhanced Road Damage Detection With Federated Learning Across Diverse And Heterogeneous Global Datasets	32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024 (2024)
46726	0.871	Dwivedi S.K.; Arya D.; Sekimoto Y.	Road Damage Detection Across Borders: Federated Learning Insights From Japan, China, Norway And The Usa	Proceedings - 2024 IEEE Smart World Congress, SWC 2024 - 2024 IEEE Ubiquitous Intelligence and Computing, Autonomous and Trusted Computing, Digital Twin, Metaverse, Privacy Computing and Data Security, Scalable Computing and Communications (2024)
41514	0.871	Alzamzami O.; Babour A.; Baalawi W.; Al Khuzayem L.	Pds-Uav: A Deep Learning-Based Pothole Detection System Using Unmanned Aerial Vehicle Images	Sustainability (Switzerland), 16, 21 (2024)