  Pervasive computing promotes the installation of connected devices in our
living spaces in order to provide services. Two major developments have gained
significant momentum recently: an advanced use of edge resources and the
integration of machine learning techniques for engineering applications. This
evolution raises major challenges, in particular related to the appropriate
distribution of computing elements along an edge-to-cloud continuum. About
this, Federated Learning has been recently proposed for distributed model
training in the edge. The principle of this approach is to aggregate models
learned on distributed clients in order to obtain a new, more general model.
The resulting model is then redistributed to clients for further training. To
date, the most popular federated learning algorithm uses coordinate-wise
averaging of the model parameters for aggregation. However, it has been shown
that this method is not adapted in heterogeneous environments where data is not
identically and independently distributed (non-iid). This corresponds directly
to some pervasive computing scenarios where heterogeneity of devices and users
challenges machine learning with the double objective of generalization and
personalization. In this paper, we propose a novel aggregation algorithm,
termed FedDist, which is able to modify its model architecture (here, deep
neural network) by identifying dissimilarities between specific neurons amongst
the clients. This permits to account for clients' specificity without impairing
generalization. Furthermore, we define a complete method to evaluate federated
learning in a realistic way taking generalization and personalization into
account.
  Using this method, FedDist is extensively tested and compared with three
state-of-the-art federated learning algorithms on the pervasive domain of Human
Activity Recognition with smartphones.
