The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Privacy-Preserving Serverless Computing Using Federated Learning for Smart Grids
Abstract
The smart power grid is a critical energy infrastructure where real-time electricity usage data is collected to predict future energy requirements. The existing prediction models focus on the centralized frameworks, where the collected data from various home area networks (HANs) are forwarded to a central server. This process leads to cybersecurity threats. This article proposes a federated learning based model with privacy preservation of smart grids data using serverless cloud computing. The model considers the blockchain-enabled dew servers in each HAN for local data storage and local model training. Advanced perturbation and normalization techniques are used to reduce the inverse impact of irregular workload on the training results. The experiment conducted on benchmarks datasets demonstrates that the proposed model minimizes the computation and communication costs, attacking probability, and improves the test accuracy. Overall, the proposed model enables smart grids with robust privacy preservation and high accuracy.
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Cites
Towards Fair and Privacy-Preserving Federated Deep Models
Lyu, L., Yu, J., Nandakumar, K., Li, Y., Ma, X., Jin, J., Yu, H., & Ng, K. S. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.2996273 · Cited by 250 · cited 1×
“As we follow the experiment setup of Fair and privacy-preserving deep learning (FPPDL) framework [25], we took 100 000 and 10 000 images for the training and testing purpose, respectively.”
Method
Flagged first, then by citations
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Cited by
Sorted by citations
Deepfake detection using deep learning methods: A systematic and comprehensive review
Heidari, A., Jafari Navimipour, N., Dag, H., & Unal, M. (2024) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1520 · Cited by 349
“Deepfake detection is typically considered a binary arrangement problem in which classifiers are used to distinguish between reliable and interfering movies (Singh et al., 2021).”
Background
Metadata
- Authors
- Singh, P., Masud, M., Hossain, M. S., Kaur, A., Muhammad, G., & Ghoneim, A.
- Affiliations
- Lovely Professional University (IN)
- Taif University (SA)
- King Saud University (SA)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2022-11
- Volume
- 18
- Issue
- 11
- Pages
- 7843-7852
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W3214163130
- Cited by
- 58
- References
- 1
- Citations by year
- 2026: 10 · 2025: 18 · 2024: 19 · 2023: 9 · 2022: 2
- FWCI
- 2.65
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- Deanship of Scientific Research, King Saud University, Riyadh, Saudi Arabia (RGP-229)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Keywords
- privacy-preserving federated learning, serverless computing, smart grid, electricity load forecasting, home area networks, blockchain, decentralized storage, data perturbation, normalization, cyber threats, computation and communication overhead
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