FUSE: a federated learning and U-shape split learning-based electricity theft detection framework

Conclusion In this study, we propose a novel theft detection framework named FUSE. Firstly, we introduce a new variant of split learning named three-tier U-shape split learning into the local training process. This allows us to migrate the extensive computational overhead to the assisted CSs, while...

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Bibliographic Details
Published in:Science China. Information sciences 2024-04, Vol.67 (4), p.149302, Article 149302
Main Authors: Li, Xuan, Wang, Naiyu, Zhu, Liehuang, Yuan, Shuai, Guan, Zhitao
Format: Article
Language:English
Subjects:
Computer Science
Federated learning
Information Systems and Communication Service
Letter
Theft
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Summary:Conclusion In this study, we propose a novel theft detection framework named FUSE. Firstly, we introduce a new variant of split learning named three-tier U-shape split learning into the local training process. This allows us to migrate the extensive computational overhead to the assisted CSs, while ensuring the sensitive data is preserved in the place where it is generated for privacy-preserving. Furthermore, we design a two-stage semi-asynchronous aggregation mechanism to accommodate the straggler issue and associated communication overhead, which consists of cosine similarity-based pre-aggregation and staleness-aware aggregation. Finally, we conduct extensive experiments and validate our model performance through the comparisons with the benchmarks.
ISSN:1674-733X
1869-1919
DOI:10.1007/s11432-023-3946-x