  Personalized Federated Continual Learning (PFCL) is a new practical scenario
that poses greater challenges in sharing and personalizing knowledge. PFCL not
only relies on knowledge fusion for server aggregation at the global
spatial-temporal perspective but also needs model improvement for each client
according to the local requirements. Existing methods, whether in Personalized
Federated Learning (PFL) or Federated Continual Learning (FCL), have overlooked
the multi-granularity representation of knowledge, which can be utilized to
overcome Spatial-Temporal Catastrophic Forgetting (STCF) and adopt generalized
knowledge to itself by coarse-to-fine human cognitive mechanisms. Moreover, it
allows more effectively to personalized shared knowledge, thus serving its own
purpose. To this end, we propose a novel concept called multi-granularity
prompt, i.e., coarse-grained global prompt acquired through the common model
learning process, and fine-grained local prompt used to personalize the
generalized representation. The former focuses on efficiently transferring
shared global knowledge without spatial forgetting, and the latter emphasizes
specific learning of personalized local knowledge to overcome temporal
forgetting. In addition, we design a selective prompt fusion mechanism for
aggregating knowledge of global prompts distilled from different clients. By
the exclusive fusion of coarse-grained knowledge, we achieve the transmission
and refinement of common knowledge among clients, further enhancing the
performance of personalization. Extensive experiments demonstrate the
effectiveness of the proposed method in addressing STCF as well as improving
personalized performance. Our code now is available at
https://github.com/SkyOfBeginning/FedMGP.
