Federated Class-Incremental Learning (FCIL) focuses on continually
transferring the previous knowledge to learn new classes in dynamic Federated
Learning (FL). However, existing methods do not consider the trustworthiness of
FCIL, i.e., improving continual utility, privacy, and efficiency
simultaneously, which is greatly influenced by catastrophic forgetting and data
heterogeneity among clients. To address this issue, we propose FedProK
(Federated Prototypical Feature Knowledge Transfer), leveraging prototypical
feature as a novel representation of knowledge to perform spatial-temporal
knowledge transfer. Specifically, FedProK consists of two components: (1)
feature translation procedure on the client side by temporal knowledge transfer
from the learned classes and (2) prototypical knowledge fusion on the server
side by spatial knowledge transfer among clients. Extensive experiments
conducted in both synchronous and asynchronous settings demonstrate that our
FedProK outperforms the other state-of-the-art methods in three perspectives of
trustworthiness, validating its effectiveness in selectively transferring
spatial-temporal knowledge.

Federated Class-Incremental Learning (FCIL) aims to improve trustworthiness by transferring knowledge using FedProK, a method that leverages prototypical feature knowledge transfer, demonstrating superior performance in spatial-temporal knowledge transfer.