  Federated learning (FL) has attracted growing attention via data-private
collaborative training on decentralized clients. However, most existing methods
unrealistically assume object classes of the overall framework are fixed over
time. It makes the global model suffer from significant catastrophic forgetting
on old classes in real-world scenarios, where local clients often collect new
classes continuously and have very limited storage memory to store old classes.
Moreover, new clients with unseen new classes may participate in the FL
training, further aggravating the catastrophic forgetting of the global model.
To address these challenges, we develop a novel Global-Local Forgetting
Compensation (GLFC) model, to learn a global class incremental model for
alleviating the catastrophic forgetting from both local and global
perspectives. Specifically, to address local forgetting caused by class
imbalance at the local clients, we design a class-aware gradient compensation
loss and a class-semantic relation distillation loss to balance the forgetting
of old classes and distill consistent inter-class relations across tasks. To
tackle the global forgetting brought by the non-i.i.d class imbalance across
clients, we propose a proxy server that selects the best old global model to
assist the local relation distillation. Moreover, a prototype gradient-based
communication mechanism is developed to protect privacy. Our model outperforms
state-of-the-art methods by 4.4%-15.1% in terms of average accuracy on
representative benchmark datasets.
