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Federated Learning Architectures for Privacy-Preserving Smart Grid Data Processing

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The proposed framework enhances prediction performance and privacy in smart grid data, indicating advancements in decentralized modeling.

Federated learning framework improves prediction performance while preserving data privacy across smart grid components.
The method integrates adaptive differential privacy, gradient compression, and topology-aware aggregation.
Robustness is shown through energy profiling and temporal stability, with privacy noise within acceptable limits.
Methodology considers computational efficiency and topological heterogeneity, suggesting strong scalability for real-time energy applications.

Abdulkareem et al. (2025) studied this question.

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