  Federated Class Incremental Learning (FCIL) is a critical yet largely
underexplored issue that deals with the dynamic incorporation of new classes
within federated learning (FL). Existing methods often employ generative
adversarial networks (GANs) to produce synthetic images to address privacy
concerns in FL. However, GANs exhibit inherent instability and high
sensitivity, compromising the effectiveness of these methods. In this paper, we
introduce a novel data-free federated class incremental learning framework with
diffusion-based generative memory (DFedDGM) to mitigate catastrophic forgetting
by generating stable, high-quality images through diffusion models. We design a
new balanced sampler to help train the diffusion models to alleviate the common
non-IID problem in FL, and introduce an entropy-based sample filtering
technique from an information theory perspective to enhance the quality of
generative samples. Finally, we integrate knowledge distillation with a
feature-based regularization term for better knowledge transfer. Our framework
does not incur additional communication costs compared to the baseline FedAvg
method. Extensive experiments across multiple datasets demonstrate that our
method significantly outperforms existing baselines, e.g., over a 4%
improvement in average accuracy on the Tiny-ImageNet dataset.
