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Fed-SAD:A secure aggregation federated learning method for distributed load forecasting
  • +2
  • Jian Li,
  • Hexiao Li,
  • Ruiqi Wang,
  • Yiguo Guo,
  • Sixing Wu
Jian Li
North China Electric Power University
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Hexiao Li
North China Electric Power University
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Ruiqi Wang
State Grid Shandong Electric Power Company
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Yiguo Guo
State Grid Shandong Electric Power Company
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Sixing Wu
North China Electric Power University

Corresponding Author:[email protected]

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Abstract

The distributed and privacy-preserving characteristics of fine-grained smart grid data hinder data sharing, making federated learning an attractive approach for collaborative training among data owners with similar load patterns. However, malicious models can interfere with training in the federated learning aggregation process, making it difficult to ensure the accuracy and safety of the central model in load forecasting. Therefore, we propose a secure aggregation federated learning method for distributed load forecasting based on similarity and distance (Fed-SAD), which effectively eliminates the interference of malicious models by securely aggregating models, thereby ensuring accurate and safe distributed scenario prediction. Experimental results demonstrate that Fed-SAD maintains high accuracy and robustness in both the presence and absence of malicious models, while maintaining data and model security.
10 Jul 2023Submitted to IET Generation, Transmission & Distribution
13 Jul 2023Submission Checks Completed
13 Jul 2023Assigned to Editor
20 Jul 2023Reviewer(s) Assigned
06 Aug 2023Review(s) Completed, Editorial Evaluation Pending
16 Aug 2023Editorial Decision: Revise Major
02 Sep 20231st Revision Received
06 Sep 2023Submission Checks Completed
06 Sep 2023Assigned to Editor
06 Sep 2023Review(s) Completed, Editorial Evaluation Pending
06 Sep 2023Reviewer(s) Assigned
03 Oct 2023Editorial Decision: Accept