PP-FCL: Privacy-Preserving Federated Continual Learning via Generative Replay and Incremental Representation Enhancement

Federated Learning (FL) enables collaborative model training across multiple edge devices without sharing raw data, yet existing FL frameworks often assume static data domains, limiting their applicability to real-world scenarios where data evolves over time. To address this, Federated Continual Learning (FCL) integrates continual learning into FL, but conventional strategies such as data replay are impractical due to privacy and storage constraints. In this paper, we propose PP-FCL, a privacy-preserving FCL framework that mitigates catastrophic forgetting without storing sensitive client data. PP-FCL employs a server-side generative model to synthesize representative samples of previously learned tasks, enhancing data diversity, preserving characteristic class features, and refining decision boundaries. On the client side, an improved contrastive incremental learning loss and a carefully designed feature distillation method decouple old and new knowledge, ensuring a balanced trade-off between plasticity and stability. As a result, PP-FCL not only enhances the model’s representational capabilities but also adapts effectively to non-stationary data distributions, maintaining robust performance in privacy-sensitive, evolving federated environments. Empirical results on CIFAR-10, CIFAR-100 and TinyImageNet demonstrate that PP-FCL outperforms state-of-the-art baselines by approximately 5–6% in average accuracy. This substantial improvement highlights PP-FCL’s effectiveness in preserving model performance under evolving conditions, ensuring robust and adaptive learning in dynamic federated environments.