  In the smart grid, huge amounts of consumption data are used to train deep
learning models for applications such as load monitoring and demand response.
However, these applications raise concerns regarding security and have high
accuracy requirements. In one hand, the data used is privacy-sensitive. For
instance, the fine-grained data collected by a smart meter at a consumer's home
may reveal information on the appliances and thus the consumer's behaviour at
home. On the other hand, the deep learning models require big data volumes with
enough variety and to be trained adequately. In this paper, we evaluate the use
of Edge computing and federated learning, a decentralized machine learning
scheme that allows to increase the volume and diversity of data used to train
the deep learning models without compromising privacy. This paper reports, to
the best of our knowledge, the first use of federated learning for household
load forecasting and achieves promising results. The simulations were done
using Tensorflow Federated on the data from 200 houses from Texas, USA.
