Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives. (arXiv:2304.08602v1 [cs.LG])

Consumer's privacy is a main concern in Smart Grids (SGs) due to the
sensitivity of energy data, particularly when used to train machine learning
models for different services. These data-driven models often require huge
amounts of data to achieve acceptable performance leading in most cases to
risks of privacy leakage. By pushing the training to the edge, Federated
Learning (FL) offers a good compromise between privacy preservation and the
predictive performance of these models. The current paper presents an overview …

applications cases challenges compromise consumer current data data-driven edge energy federated learning machine machine learning machine learning models main performance perspectives preservation privacy risks services smart the edge train training