FedSTAFN: A Secure and Efficient Privacy-Preserving Federated Learning Model for Electricity Theft Detection
Song Deng; Peng Tian; Junyang Deng; Xindong Wu
- Published
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Journal / collection
- IEEE Transactions on Emerging Topics in Computational Intelligence
- Research area
- Energy & infrastructure
- Source type
- Journal article
- DOI
- 10.1109/tetci.2026.3716871
The original publisher provides the full text and its access options.
Cite this source
Song Deng; Peng Tian; Junyang Deng; Xindong Wu. FedSTAFN: A Secure and Efficient Privacy-Preserving Federated Learning Model for Electricity Theft Detection. IEEE Transactions on Emerging Topics in Computational Intelligence. 2026. https://doi.org/10.1109/tetci.2026.3716871