Personalized Federated Continual Learning via Multi-granularity Prompt
<|reference_start|>Personalized Federated Continual Learning via Multi-granularity Prompt: 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.<|reference_end|>
@article{yu2024personalized,
    title={Personalized Federated Continual Learning via Multi-granularity Prompt},
    author={Hao Yu, Xin Yang, Xin Gao, Yan Kang, Hao Wang, Junbo Zhang and Tianrui
  Li},
    journal={arXiv preprint arXiv:2407.00113},
    year={2024},
    doi={10.1145/3637528.3671948},
    archivePrefix={arXiv},
    eprint={2407.00113},
    primaryClass={cs.LG cs.AI}
}