  Federated continual learning (FCL) learns incremental tasks over time from
confidential datasets distributed across clients. This paper focuses on
rehearsal-free FCL, which has severe forgetting issues when learning new tasks
due to the lack of access to historical task data. To address this issue, we
propose Fed-CPrompt based on prompt learning techniques to obtain task-specific
prompts in a communication-efficient way. Fed-CPrompt introduces two key
components, asynchronous prompt learning, and contrastive continual loss, to
handle asynchronous task arrival and heterogeneous data distributions in FCL,
respectively. Extensive experiments demonstrate the effectiveness of
Fed-CPrompt in achieving SOTA rehearsal-free FCL performance.
