  Existing federated learning methods have effectively dealt with decentralized
learning in scenarios involving data privacy and non-IID data. However, in
real-world situations, each client dynamically learns new classes, requiring
the global model to classify all seen classes. To effectively mitigate
catastrophic forgetting and data heterogeneity under low communication costs,
we propose a simple and effective method named PILoRA. On the one hand, we
adopt prototype learning to learn better feature representations and leverage
the heuristic information between prototypes and class features to design a
prototype re-weight module to solve the classifier bias caused by data
heterogeneity without retraining the classifier. On the other hand, we view
incremental learning as the process of learning distinct task vectors and
encoding them within different LoRA parameters. Accordingly, we propose
Incremental LoRA to mitigate catastrophic forgetting. Experimental results on
standard datasets indicate that our method outperforms the state-of-the-art
approaches significantly. More importantly, our method exhibits strong
robustness and superiority in different settings and degrees of data
heterogeneity. The code is available at
\url{https://github.com/Ghy0501/PILoRA}.
