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

PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning