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