Weighted explainable federated learning for privacy-preserving and scalable energy optimization in autonomous vehicular networks
The rise of electric and autonomous vehicles in smart cities poses significant challenges in vehicular energy management, including unoptimized consumption, inefficient grid utilization, and unpredictable traffic dynamics. Traditional centralized machine learning models and cloud-based Energy Manage...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Egyptian Informatics Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866525001513 |
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| author | Muhammad Saleem Ali Arishi Muhammad Sajid Farooq M.A. Khan Khan M. Adnan |
| author_facet | Muhammad Saleem Ali Arishi Muhammad Sajid Farooq M.A. Khan Khan M. Adnan |
| author_sort | Muhammad Saleem |
| collection | DOAJ |
| description | The rise of electric and autonomous vehicles in smart cities poses significant challenges in vehicular energy management, including unoptimized consumption, inefficient grid utilization, and unpredictable traffic dynamics. Traditional centralized machine learning models and cloud-based Energy Management Systems (EMSs) often struggle with real-time adaptability, high-dimensional data processing, and privacy concerns. While Federated Learning (FL) offers a decentralized solution by enabling edge devices to collaboratively train models without sharing raw data, conventional FL typically treats all client contributions equally—regardless of their data volume, quality, or contextual relevance. This limits model generalization in heterogeneous vehicular environments. To address this, we propose a Weighted Explainable Federated Learning (WEFL) framework that enhances conventional FL by assigning dynamic importance to client updates based on factors such as data relevance and local model performance. The framework also integrates Explainable AI (XAI) methods to improve interpretability, transparency, and regulatory compliance. The proposed WEFL-XAI model ensures privacy-preserving, real-time, and adaptive vehicular energy optimization, leveraging traffic patterns, vehicle energy states, and grid load conditions. Experimental evaluations demonstrate that our Multi-Layer Perceptron (MLP)-based Weighted Federated Learning (WFL) model achieves an R2 of 86.84% for energy consumption and 74.16 % for traffic density, reflecting a strong trade-off between performance and privacy. While these values are lower than centralized MLP benchmarks, the WFL model outperforms standard FL baselines by offering enhanced privacy preservation, interpretability, and decentralized scalability, making it a more viable choice for real-world smart mobility deployments. |
| format | Article |
| id | doaj-art-8728b08943f141c89281df7259348564 |
| institution | Kabale University |
| issn | 1110-8665 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-8728b08943f141c89281df72593485642025-08-22T04:55:48ZengElsevierEgyptian Informatics Journal1110-86652025-09-013110075810.1016/j.eij.2025.100758Weighted explainable federated learning for privacy-preserving and scalable energy optimization in autonomous vehicular networksMuhammad Saleem0Ali Arishi1Muhammad Sajid Farooq2M.A. Khan3Khan M. Adnan4Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab 140401, IndiaDepartment of Industrial Engineering, King Khalid University, Abha 61421, Saudi Arabia; Center for Engineering and Technology Innovations, King Khalid University, Abha 61421, Saudi ArabiaDepartment of Cyber Security, NASTP Institute of Information Technology, Lahore 58810, PakistanSchool of Computing, Horizon University College, Ajman, United Arab Emirates; Applied Science Research Center, Applied Science Private University, Amman, Jordan; Riphah School of Computing & Innovation, Faculty of Computing, Riphah International University, Lahore Campus, 54000, PakistanDepartment of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si 13120, Republic of Korea; Corresponding author.The rise of electric and autonomous vehicles in smart cities poses significant challenges in vehicular energy management, including unoptimized consumption, inefficient grid utilization, and unpredictable traffic dynamics. Traditional centralized machine learning models and cloud-based Energy Management Systems (EMSs) often struggle with real-time adaptability, high-dimensional data processing, and privacy concerns. While Federated Learning (FL) offers a decentralized solution by enabling edge devices to collaboratively train models without sharing raw data, conventional FL typically treats all client contributions equally—regardless of their data volume, quality, or contextual relevance. This limits model generalization in heterogeneous vehicular environments. To address this, we propose a Weighted Explainable Federated Learning (WEFL) framework that enhances conventional FL by assigning dynamic importance to client updates based on factors such as data relevance and local model performance. The framework also integrates Explainable AI (XAI) methods to improve interpretability, transparency, and regulatory compliance. The proposed WEFL-XAI model ensures privacy-preserving, real-time, and adaptive vehicular energy optimization, leveraging traffic patterns, vehicle energy states, and grid load conditions. Experimental evaluations demonstrate that our Multi-Layer Perceptron (MLP)-based Weighted Federated Learning (WFL) model achieves an R2 of 86.84% for energy consumption and 74.16 % for traffic density, reflecting a strong trade-off between performance and privacy. While these values are lower than centralized MLP benchmarks, the WFL model outperforms standard FL baselines by offering enhanced privacy preservation, interpretability, and decentralized scalability, making it a more viable choice for real-world smart mobility deployments.http://www.sciencedirect.com/science/article/pii/S1110866525001513Vehicular energy managementWeighted explainable federated learningXAIEnergy Management Systems (EMSs)Smart city |
| spellingShingle | Muhammad Saleem Ali Arishi Muhammad Sajid Farooq M.A. Khan Khan M. Adnan Weighted explainable federated learning for privacy-preserving and scalable energy optimization in autonomous vehicular networks Egyptian Informatics Journal Vehicular energy management Weighted explainable federated learning XAI Energy Management Systems (EMSs) Smart city |
| title | Weighted explainable federated learning for privacy-preserving and scalable energy optimization in autonomous vehicular networks |
| title_full | Weighted explainable federated learning for privacy-preserving and scalable energy optimization in autonomous vehicular networks |
| title_fullStr | Weighted explainable federated learning for privacy-preserving and scalable energy optimization in autonomous vehicular networks |
| title_full_unstemmed | Weighted explainable federated learning for privacy-preserving and scalable energy optimization in autonomous vehicular networks |
| title_short | Weighted explainable federated learning for privacy-preserving and scalable energy optimization in autonomous vehicular networks |
| title_sort | weighted explainable federated learning for privacy preserving and scalable energy optimization in autonomous vehicular networks |
| topic | Vehicular energy management Weighted explainable federated learning XAI Energy Management Systems (EMSs) Smart city |
| url | http://www.sciencedirect.com/science/article/pii/S1110866525001513 |
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