Privacy Protection and Efficient Energy Metering through Federated Learning in Smart Homes

This study explores using FL in smart energy metering in connected homes, focusing on user privacy protection. It analyzes how FL and clustering techniques can improve the accuracy of energy consumption prediction models, considering variables such as the presence of solar panels, household appliances, electric vehicles, and the number of inhabitants. Furthermore, possible privacy attack mechanisms in this context are investigated, such as identifying individual devices from aggregated data from a single smart meter. This highlights the need for privacy-safeguarding solutions in advanced metering scenarios.