  The standard class-incremental continual learning setting assumes a set of
tasks seen one after the other in a fixed and predefined order. This is not
very realistic in federated learning environments where each client works
independently in an asynchronous manner getting data for the different tasks in
time-frames and orders totally uncorrelated with the other ones. We introduce a
novel federated learning setting (AFCL) where the continual learning of
multiple tasks happens at each client with different orderings and in
asynchronous time slots. We tackle this novel task using prototype-based
learning, a representation loss, fractal pre-training, and a modified
aggregation policy. Our approach, called FedSpace, effectively tackles this
task as shown by the results on the CIFAR-100 dataset using 3 different
federated splits with 50, 100, and 500 clients, respectively. The code and
federated splits are available at https://github.com/LTTM/FedSpace.
