Privacy Preserving Federated Learning for Energy Disaggregation of Smart Homes

Smart advanced metering infrastructure and edge devices show promising solutions in digitalising distributed energy systems. Energy disaggregation of household load consumption provides a better understanding of consumers’ appliance-level usage patterns. Machine learning approaches enhance the power system's efficiency but this is contingent upon sufficient training samples for efficient and accurate prediction tasks. In a centralised setup, transferring such a substantially high volume of information to the cloud server has a communication bottleneck. Although high-computing edge devices seek to address such problems, the data scarcity and heterogeneity among clients remain challenges to be addressed. Federated learning offers a compelling solution in such a scenario by leveraging the ML model training at edge devices and aggregating the client's updates at a cloud server. However, FL still faces significant security issues, including the potential eavesdropping by a malicious actor with the intention of stealing clients' information while communicating with an honest-but-curious server. The study aims to secure the sensitive information of energy users participating in the nonintrusive load monitoring (NILM) program by integrating differential privacy with a personalised federated learning approach. The Fisher information method was adapted to extract the global model information based on common features, while personalised updates will not be shared with the server for client-specific features. Similarly, the authors employed an adaptive differential privacy only on the shared local updates (DP-PFL) while communicating with the server. Experimental results on the Pecan Street and REFIT datasets depict that DP-PFL exhibits more favourable performance on both the energy prediction and status classification tasks compared to other state-of-the-art DP approaches in federated NILM.