  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.
