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Machine learning (ML) based smart meter data analytics is very promising forenergy management and demand-response applications in the advanced meteringinfrastructure(AMI). A key challenge in developing distributed ML applicationsfor AMI is to preserve user privacy while allowing active end-usersparticipation. This paper addresses this challenge and proposes aprivacy-preserving federated learning framework for ML applications in the AMI.We consider each smart meter as a federated edge device hosting an MLapplication that exchanges information with a central aggregator or a dataconcentrator, periodically. Instead of transferring the raw data sensed by thesmart meters, the ML model weights are transferred to the aggregator topreserve privacy. The aggregator processes these parameters to devise a robustML model that can be substituted at each edge device. We also discussstrategies to enhance privacy and improve communication efficiency whilesharing the ML model parameters, suited for relatively slow network connectionsin the AMI. We demonstrate the proposed framework on a use case federated ML(FML) application that improves short-term load forecasting (STLF). We use along short-term memory(LSTM) recurrent neural network (RNN) model for STLF. Inour architecture, we assume that there is an aggregator connected to a group ofsmart meters. The aggregator uses the learned model gradients received from thefederated smart meters to generate an aggregate, robust RNN model whichimproves the forecasting accuracy for individual and aggregated STLF. Ourresults indicate that with FML, forecasting accuracy is increased whilepreserving the data privacy of the end-users.

AMI-FML: A Privacy-Preserving Federated Machine Learning Framework for AMI