As Web technology continues to develop, it has become increasingly common to use data stored on different clients. At the same time, federated learning has received widespread attention due to its ability to protect data privacy when let models learn from data which is distributed across various clients. However, most existing works assume that the client's data are fixed. In real-world scenarios, such an assumption is most likely not true as data may be continuously generated and new classes may also appear. To this end, we focus on the practical and challenging federated class-incremental learning (FCIL) problem. For FCIL, the local and global models may suffer from catastrophic forgetting on old classes caused by the arrival of new classes and the data distributions of clients are non-independent and identically distributed (non-iid). In this paper, we propose a novel method called Federated Class-Incremental Learning with PrompTing (FCILPT). Given the privacy and limited memory, FCILPT does not use a rehearsal-based buffer to keep exemplars of old data. We choose to use prompts to ease the catastrophic forgetting of the old classes. Specifically, we encode the task-relevant and task-irrelevant knowledge into prompts, preserving the old and new knowledge of the local clients and solving the problem of catastrophic forgetting. We first sort the task information in the prompt pool in the local clients to align the task information on different clients before global aggregation. It ensures that the same task's knowledge are fully integrated, solving the problem of non-iid caused by the lack of classes among different clients in the same incremental task. Experiments on CIFAR-100, Mini-ImageNet, and Tiny-ImageNet demonstrate that FCILPT achieves significant accuracy improvements over the state-of-the-art methods.

翻译：摘要：随着网络技术的持续发展，利用存储于不同客户端的数据已变得愈发普遍。与此同时，联邦学习因其在模型从分布于各客户端的数据中学习时能够保护数据隐私而受到广泛关注。然而，现有研究大多假设客户端数据是固定不变的。在现实场景中，这一假设很可能不成立，因为数据可能持续生成并出现新类别。为此，我们关注实用且具挑战性的联邦类增量学习（FCIL）问题。在FCIL中，本地和全局模型可能因新类别的出现而对旧类别产生灾难性遗忘，同时客户端数据分布呈非独立同分布（non-iid）特性。本文提出一种名为基于提示的联邦类增量学习（FCILPT）的新方法。考虑到隐私限制与有限内存，FCILPT不使用基于重放的缓冲区来保存旧数据样本。我们选择利用提示来缓解旧类别的灾难性遗忘。具体而言，我们将任务相关与任务无关知识编码至提示中，保留本地客户端的旧知识与新知识，从而解决灾难性遗忘问题。在全局聚合前，我们首先对本地客户端提示池中的任务信息进行排序，以对齐不同客户端的任务信息，确保同一任务的知识被充分集成，解决因相同增量任务中不同客户端缺失类别导致的非独立同分布问题。在CIFAR-100、Mini-ImageNet和Tiny-ImageNet上的实验表明，FCILPT相比现有最优方法实现了显著的精度提升。