Abstract
Edge intelligence, i.e., the execution of Machine Learning (ML) algorithms in computing resources at the edge, provides unprecedented benefits for applications in different verticals regarding data privacy, bandwidth, costs, and latency. Non-Intrusive Load Monitoring (NILM) is an application in the smart grid technology domain that could benefit from the advancements in edge intelligence to ensure consumer data privacy and decrease implementation costs. This paper proposes a federated learning-based transformer architecture for the NILM of energy-intensive residential devices, i.e., Heat Pumps (HP). We evaluate the architecture on an open-source dataset, showcasing that the performance does not deteriorate significantly compared to the centralized and is robust against the distribution shifts between the training and inference datasets and the increasing heterogeneity level between clients in the training data.
