  Machine learning (ML) based smart meter data analytics is very promising for
energy management and demand-response applications in the advanced metering
infrastructure(AMI). A key challenge in developing distributed ML applications
for AMI is to preserve user privacy while allowing active end-users
participation. This paper addresses this challenge and proposes a
privacy-preserving federated learning framework for ML applications in the AMI.
We consider each smart meter as a federated edge device hosting an ML
application that exchanges information with a central aggregator or a data
concentrator, periodically. Instead of transferring the raw data sensed by the
smart meters, the ML model weights are transferred to the aggregator to
preserve privacy. The aggregator processes these parameters to devise a robust
ML model that can be substituted at each edge device. We also discuss
strategies to enhance privacy and improve communication efficiency while
sharing the ML model parameters, suited for relatively slow network connections
in the AMI. We demonstrate the proposed framework on a use case federated ML
(FML) application that improves short-term load forecasting (STLF). We use a
long short-term memory(LSTM) recurrent neural network (RNN) model for STLF. In
our architecture, we assume that there is an aggregator connected to a group of
smart meters. The aggregator uses the learned model gradients received from the
federated smart meters to generate an aggregate, robust RNN model which
improves the forecasting accuracy for individual and aggregated STLF. Our
results indicate that with FML, forecasting accuracy is increased while
preserving the data privacy of the end-users.
