  Deep learning models often suffer from forgetting previously learned
information when trained on new data. This problem is exacerbated in federated
learning (FL), where the data is distributed and can change independently for
each user. Many solutions are proposed to resolve this catastrophic forgetting
in a centralized setting. However, they do not apply directly to FL because of
its unique complexities, such as privacy concerns and resource limitations. To
overcome these challenges, this paper presents a framework for
$\textbf{federated class incremental learning}$ that utilizes a generative
model to synthesize samples from past distributions. This data can be later
exploited alongside the training data to mitigate catastrophic forgetting. To
preserve privacy, the generative model is trained on the server using data-free
methods at the end of each task without requesting data from clients. Moreover,
our solution does not demand the users to store old data or models, which gives
them the freedom to join/leave the training at any time. Additionally, we
introduce SuperImageNet, a new regrouping of the ImageNet dataset specifically
tailored for federated continual learning. We demonstrate significant
improvements compared to existing baselines through extensive experiments on
multiple datasets.
