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
Federated Continual Learning (FCL) integrates federated learning and continual learning to address the challenge of data privacy and silos, by fusing heterogeneous knowledge from different clients and retaining knowledge of previous tasks while learning on new ones, through methods such as synchronous FCL and asynchronous FCL.