  Federated Learning, a new machine learning paradigm enhancing the use of edge
devices, is receiving a lot of attention in the pervasive community to support
the development of smart services. Nevertheless, this approach still needs to
be adapted to the specificity of the pervasive domain. In particular, issues
related to continual learning need to be addressed. In this paper, we present a
distillation-based approach dealing with catastrophic forgetting in federated
learning scenario. Specifically, Human Activity Recognition tasks are used as a
demonstration domain.
