Fed‐SAD: A secure aggregation federated learning method for distributed short‐term load forecasting

Fed‐SAD: A secure aggregation federated learning method for distributed short‐term load forecasting

Abstract The distributed and privacy‐preserving attributes of fine‐grained smart grid data create obstacles to data sharing. As a result, federated learning emerges as an effective strategy for collaborative training in distributed load forecasting. However, poisoning attacks can interfere with trai...

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Bibliographic Details
Main Authors: Hexiao Li, Sixing Wu, Ruiqi Wang, Yiguo Guo, Jianbin Li
Format: Article
Language:English
Published: Wiley 2023-11-01
Series:IET Generation, Transmission & Distribution
Subjects:
computational Intelligence
distributed algorithms
power grids
Online Access:https://doi.org/10.1049/gtd2.13022

Internet

https://doi.org/10.1049/gtd2.13022

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