Privacy-Preserving Machine Learning Algorithms for Distributed Smart Grid Systems Using Blockchain Technology

The shift towards a Distributed Smart Grid with several smart meters and Distributed Energy Resources—a smart grid via the Internet—helps obtain and analyze vast amounts of sensitive consumer data. This helps in sophisticated functions such as predicting consumer demand and identifying irregularities in consumption. The use of Machine Learning and the data poses challenges for privacy and security, particularly when predicting user behavior based on disaggregated consumption data. This paper develops a robust framework that integrates Blockchain technology with privacy-preserving machine learning, such as Federated Learning with Homomorphic Encryption or Differential Privacy. Federated Learning enables local data utility on customer devices to train distributed ML models while keeping the raw data decentralized. Blockchain helps secure model aggregation by enabling logging of private transactions, mitigating threats such as model poisoning and data integrity issues, and eliminating the need for a trusted third party. Our results focus on the critical privacy-utility-efficiency trade-off in the system, which measures the ML model's computational and accuracy latency costs. The results show that the integrated system achieves high model utility for grid services while providing scalable privacy guarantees.

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