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Differential Private Federated Learning for Privacy-Preserving Third Party Service Framework in Advanced Metering Infrastructure

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The advanced metering infrastructure (AMI) is a compulsory component of the future smart grid; it not only provides near real-time two-way communication between the consumers and the utility but also gives an opportunity to third parties to provide relevant value-added services to the consumers to improve the user satisfaction. However, existing services require the consumers share their private energy data with other parties, which has potential privacy risks. To better balance the excellent quality of the services and privacy guarantee, a novel differential private federated learning-based third-party service platform is proposed. Instead of sending the original energy data to the cloud server, the central server in the proposed scheme only collects the model parameters, which are trained locally inside the consumers’ houses. Then the collected parameters are aggregated differential privately to eliminate the identity of individuals, and the aggregated parameters are used to update the central model and improve the model performance. Furthermore, a novel attention-based bidirectional long short-term memory neural network model is adopted to make predictions. In the case study, a residential short term load forecasting task is implemented to evaluate the performance of the proposed model; from the simulation results, the conclusion is made that the proposed model can achieve similar accuracy as the typical centralized model and better control the privacy loss flexibly at the same time.

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Email Address of Submitting Author

xiaoyu.zhang@rhul.ac.uk

ORCID of Submitting Author

0000-0002-0209-531X

Submitting Author's Institution

Royal Holloway, University of London

Submitting Author's Country

United Kingdom