Privacy‐Aware Microgrid Networks With Federated Data Analytics

Abstract

Microgrid networks are crucial for enhancing energy resilience and efficiency. Effective operation and optimization of microgrids rely heavily on analyzing multidimensional data, including user consumption patterns, generation profiles, and grid status information. However, this sensitive data often originate from numerous independent participants (e.g., consumers, prosumers, and operators), posing significant privacy challenges. Federated data analytics is a suitable paradigm for collaborative analysis without centralizing raw data, while differential privacy (DP) can further provide formal data privacy guarantees. This work addresses the need for privacy‐preserving multi‐target data aggregation in federated microgrid analytics. We leverage the shuffle model of differential privacy, known for its favorable privacy‐utility trade‐offs in decentralized environments. We propose a protocol where each participant (e.g., smart meter or user device) simulates multiple virtual users to report target functions through distinct, anonymized messages. This approach improves utility for tested multi‐target aggregation tasks compared to representative decentralized DP baselines, simplifies privacy amplification analysis through group privacy properties, and matches the central‐DP error order only in the high‐communication regime specified by our utility analysis. Empirical validation using synthetic energy consumption datasets demonstrates a 40%–80% reduction in aggregation error in the tested settings, relative to the single‐message shuffle baseline.