Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Upadhye, Truptee; Nanjundan, Preethi Title A name given to the resource An Interpretable Federated Multi-Task Learning Framework for Smart Traffic Management with Hessian-Driven Optimization Insights Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the 6th International Conference on Electronics and Sustainable Communication Systems, ICESC 2025;pp.752-758 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICESC65114.2025.11212503" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICESC65114.2025.11212503</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022594263?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022594263?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Upadhye T., CHRIST University, Department of Data Science, Bangalore, India; Nanjundan P., CHRIST University, Department of Data Science, Bangalore, India Description An account of the resource Smart traffic management faces challenges in balancing privacy, interpretability, and optimization robustness, particularly when using deep learning for vehicle detection and traffic prediction. Existing methods struggle to provide transparent feature attribution while preserving data confidentiality in decentralized settings. This study proposes a federated multi-task learning (FMTL) framework based on YOLOv10, trained on an original traffic dataset, to address these limitations. The framework simultaneously performs vehicle detection, traffic density analysis, and no-entry sign identification, while employing Grad-CAM to enhance interpretability and Hessian-based eigenvalue analysis to evaluate optimization complexity. Results demonstrate an average mean accuracy of 89.7% across three real-world locations, with Grad-CAM revealing meaningful focus on vehicle density and intersection geometry. Hessian analysis confirms the presence of mixed-sign eigenvalues, proving the non-convexity of the optimization surface and highlighting convergence challenges. These outcomes establish a privacypreserving, interpretable, and optimization-aware framework for real-world smart traffic management. 2025 IEEE. Subject The topic of the resource Federated multi-task learning; Grad-CAM based feature importance; Hessian matrix analysis; multi-task feature extraction; non-convex optimization Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155503-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online