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.

翻译：联邦类增补学习（Federated Class-Incremental Learning, FCIL）致力于在动态联邦学习（Federated Learning, FL）中持续迁移先前知识以学习新类别。然而，现有方法未考虑FCIL的可信性，即同时提升持续效用、隐私性和效率，而这会受到灾难性遗忘和客户端数据异质性的显著影响。为解决该问题，本文提出FedProK（联邦原型特征知识迁移），将原型特征作为知识的新颖表征，以实现时空知识迁移。具体而言，FedProK包含两个组件：（1）客户端基于时序知识迁移从已学习类别中进行特征转换过程；（2）服务端基于客户端间空间知识迁移进行原型知识融合。在同步与异步两种设置下开展的大量实验表明，我们的FedProK在可信性的三个维度上均优于其他当前最优方法，验证了其在选择性迁移时空知识方面的有效性。