Fed-SAD:A secure aggregation federated learning method for distributed
load forecasting
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.