Secure short-term load forecasting for smart grids with
  transformer-based federated learning
Jonas Sievers Thomas Blank
Electricity load forecasting is an essential task within smart grids to
assist demand and supply balance. While advanced deep learning models require
large amounts of high-resolution data for accurate short-term load predictions,
fine-grained load profiles can expose users' electricity consumption behaviors,
which raises privacy and security concerns. One solution to improve data
privacy is federated learning, where models are trained locally on private
data, and only the trained model parameters are merged and updated on a global
server. Therefore, this paper presents a novel transformer-based deep learning
approach with federated learning for short-term electricity load prediction. To
evaluate our results, we benchmark our federated learning architecture against
central and local learning and compare the performance of our model to long
short-term memory models and convolutional neural networks. Our simulations are
based on a dataset from a German university campus and show that
transformer-based forecasting is a promising alternative to state-of-the-art
models within federated learning.