PrivEdge: a hybrid split–federated learning framework for real-time electricity theft detection on edge nodes
- DOI
- 10.1038/s41598-026-39064-8
- Published
- 2026-03-21
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-39064-8,
title = {PrivEdge: a hybrid split–federated learning framework for real-time electricity theft detection on edge nodes},
author = {Ahmed Ramadan and Marwa A. Shouman and Gamal Attiya and A. S. ZeinEl Din and Elhossiny Ibrahim},
year = {2026},
journal = {Scientific Reports},
doi = {10.1038/s41598-026-39064-8},
url = {https://doi.org/10.1038/s41598-026-39064-8}
}RIS
TY - JOUR TI - PrivEdge: a hybrid split–federated learning framework for real-time electricity theft detection on edge nodes AU - Ahmed Ramadan AU - Marwa A. Shouman AU - Gamal Attiya AU - A. S. ZeinEl Din AU - Elhossiny Ibrahim PY - 2026 JO - Scientific Reports DO - 10.1038/s41598-026-39064-8 UR - https://doi.org/10.1038/s41598-026-39064-8 ER -
APA
Ramadan, A., Shouman, M. A., Attiya, G., Din, A. S. Z., & Ibrahim, E. (2026). PrivEdge: a hybrid split–federated learning framework for real-time electricity theft detection on edge nodes. Scientific Reports. https://doi.org/10.1038/s41598-026-39064-8
Source records
- crossref · retrieved 2026-09-25T17:32:32.588Z