ItemConference Paper

An Interpretable Federated Multi-Task Learning Framework for Smart Traffic Management with Hessian-Driven Optimization Insights

Title
An Interpretable Federated Multi-Task Learning Framework for Smart Traffic Management with Hessian-Driven Optimization Insights
Creator
Upadhye, Truptee; Nanjundan, Preethi
Description
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.
Source
Proceedings of the 6th International Conference on Electronics and Sustainable Communication Systems, ICESC 2025;pp.752-758
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Federated multi-task learning; Grad-CAM based feature importance; Hessian matrix analysis; multi-task feature extraction; non-convex optimization
Coverage
Upadhye T., CHRIST University, Department of Data Science, Bangalore, India; Nanjundan P., CHRIST University, Department of Data Science, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155503-0;
Format
online
Language
English
Type
Conference paper

Citation

Upadhye, Truptee; Nanjundan, Preethi, “An Interpretable Federated Multi-Task Learning Framework for Smart Traffic Management with Hessian-Driven Optimization Insights,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25998.

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