  Federated Learning (FL) systems evolve in heterogeneous and ever-evolving
environments that challenge their performance. Under real deployments, the
learning tasks of clients can also evolve with time, which calls for the
integration of methodologies such as Continual Learning. To enable research
reproducibility, we propose a set of experimental best practices that precisely
capture and emulate complex learning scenarios. Our framework, Freddie, is the
first entirely configurable framework for Federated Continual Learning (FCL),
and it can be seamlessly deployed on a large number of machines thanks to the
use of Kubernetes and containerization. We demonstrate the effectiveness of
Freddie on two use cases, (i) large-scale FL on CIFAR100 and (ii) heterogeneous
task sequence on FCL, which highlight unaddressed performance challenges in FCL
scenarios.
