Exploring Lightweight Federated Learning for Distributed Load
  Forecasting
Abhishek Duttagupta Jin Zhao Shanker Shreejith
Federated Learning (FL) is a distributed learning scheme that enables deep
learning to be applied to sensitive data streams and applications in a
privacy-preserving manner. This paper focuses on the use of FL for analyzing
smart energy meter data with the aim to achieve comparable accuracy to
state-of-the-art methods for load forecasting while ensuring the privacy of
individual meter data. We show that with a lightweight fully connected deep
neural network, we are able to achieve forecasting accuracy comparable to
existing schemes, both at each meter source and at the aggregator, by utilising
the FL framework. The use of lightweight models further reduces the energy and
resource consumption caused by complex deep-learning models, making this
approach ideally suited for deployment across resource-constrained smart meter
systems. With our proposed lightweight model, we are able to achieve an overall
average load forecasting RMSE of 0.17, with the model having a negligible
energy overhead of 50 mWh when performing training and inference on an Arduino
Uno platform.