Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal International Journal of Engineering, Science and Information Technology
T.Y. Alfalahi, Saad
Unknown Affiliation
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Federated Learning Architectures for Privacy-Preserving Smart Grid Data Processing Abdulkareem, Sarah Ali; M. Kallow, Sabah; Bako, Imad Matti; Abdullah, Salima Baji; T.Y. Alfalahi, Saad; Batumalay, M.
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1423

Abstract

The use of smart data in smart grid infrastructure has lately become essential for efficient power distribution, instantaneous?decision-making and overall system protection. Nonetheless, the application of centralized machine-learned models is impeded by?privacy issues, nonhomogeneous distributed data sources, and communication constraints. In this paper, we propose a federated learning framework to handle these challenges and support decentralized, privacy-preserving?model training across a wide range of smart grid components such as residential meters, substations, and electric vehicle charging stations. The proposed method develops a multi-staged framework, which includes adaptive differential privacy, gradient compression, and topology-aware aggregation to improve?the model's performance in the meanwhile of data privacy. The robustness of the system is demonstrated by energy profiling, cross-domain generalization test and temporal?stability analysis. Findings indicate the model has good prediction performance across different grid setups and customer profiles and that energy use and privacy?noise are within acceptable limits for operational use. Furthermore, the architecture shows?strong generalization to unseen domains, and robust performance through many federated training rounds. By considering?computational efficiency, privacy limitations and topological heterogeneity, this work provides a scalable and secure real-time energy intelligence approach. Results suggest that federated?learning with adaptations to the smart grid is a promising approach for robust privacy-preserving analytics applied to critical infrastructures. This work will support energy efficiency in the future which will be a process innovation. 
Co-Authors Abdulkareem, Sarah Ali Abdullah, Salima Baji Bako, Imad Matti Batumalay, M. M. Kallow, Sabah