Federated Learning Architectures for Privacy-Preserving Smart Grid Data Processing

The use of smart data in smart grid infrastructure has lately become essential for efficient power distribution, instantaneous?decision-making and overall system protection. Nonetheless, the application of centralized machine-learned models is impeded by?privacy issues, nonhomogeneous distributed data sources, and communication constraints. In this paper, we propose a federated learning framework to handle these challenges and support decentralized, privacy-preserving?model training across a wide range of smart grid components such as residential meters, substations, and electric vehicle charging stations. The proposed method develops a multi-staged framework, which includes adaptive differential privacy, gradient compression, and topology-aware aggregation to improve?the model's performance in the meanwhile of data privacy. The robustness of the system is demonstrated by energy profiling, cross-domain generalization test and temporal?stability analysis. Findings indicate the model has good prediction performance across different grid setups and customer profiles and that energy use and privacy?noise are within acceptable limits for operational use. Furthermore, the architecture shows?strong generalization to unseen domains, and robust performance through many federated training rounds. By considering?computational efficiency, privacy limitations and topological heterogeneity, this work provides a scalable and secure real-time energy intelligence approach. Results suggest that federated?learning with adaptations to the smart grid is a promising approach for robust privacy-preserving analytics applied to critical infrastructures. This work will support energy efficiency in the future which will be a process innovation.