  Federated Class-Incremental Learning (FCIL) increasingly becomes important in
the decentralized setting, where it enables multiple participants to
collaboratively train a global model to perform well on a sequence of tasks
without sharing their private data. In FCIL, conventional Federated Learning
algorithms such as FedAVG often suffer from catastrophic forgetting, resulting
in significant performance declines on earlier tasks. Recent works, based on
generative models, produce synthetic images to help mitigate this issue across
all classes, but these approaches' testing accuracy on previous classes is
still much lower than recent classes, i.e., having better plasticity than
stability. To overcome these issues, this paper presents Federated Global Twin
Generator (FedGTG), an FCIL framework that exploits privacy-preserving
generative-model training on the global side without accessing client data.
Specifically, the server trains a data generator and a feature generator to
create two types of information from all seen classes, and then it sends the
synthetic data to the client side. The clients then use
feature-direction-controlling losses to make the local models retain knowledge
and learn new tasks well. We extensively analyze the robustness of FedGTG on
natural images, as well as its ability to converge to flat local minima and
achieve better-predicting confidence (calibration). Experimental results on
CIFAR-10, CIFAR-100, and tiny-ImageNet demonstrate the improvements in accuracy
and forgetting measures of FedGTG compared to previous frameworks.
