  Federated Class Incremental Learning (FCIL) is a new direction in continual
learning (CL) for addressing catastrophic forgetting and non-IID data
distribution simultaneously. Existing FCIL methods call for high communication
costs and exemplars from previous classes. We propose a novel rehearsal-free
method for FCIL named prototypes-injected prompt (PIP) that involves 3 main
ideas: a) prototype injection on prompt learning, b) prototype augmentation,
and c) weighted Gaussian aggregation on the server side. Our experiment result
shows that the proposed method outperforms the current state of the arts
(SOTAs) with a significant improvement (up to 33%) in CIFAR100, MiniImageNet
and TinyImageNet datasets. Our extensive analysis demonstrates the robustness
of PIP in different task sizes, and the advantage of requiring smaller
participating local clients, and smaller global rounds. For further study,
source codes of PIP, baseline, and experimental logs are shared publicly in
https://github.com/anwarmaxsum/PIP.
