federated learning is a technique that enables a centralized server to learn
from distributed clients via communications without accessing the client local
data. However, existing federated learning works mainly
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.