Regularization-Enhanced Federated Class-Incremental Learning with Generative Replay for Smart Edge

Abstract

Federated learning is an emerging paradigm that addresses privacy concerns by training on decentralized data, but it faces significant performance challenges due to factors like non-independent and identically distributed (non-IID) data and dynamic data distributions. These challenges are further amplified in federated class-incremental learning under realistic, non-monotonic class streams, where previously seen classes can intermittently reappear alongside new classes—making generative replay highly susceptible to training instability, mode collapse, and feature drift. To address these issues, we propose RAVEL-FCL, a generative replay-based framework for federated continual learning. Our approach integrates an improved generative model based on Rebooting ACGAN with multi-level feature alignment to ensure consistency, and employs Elastic Variational Continual Learning on the server to probabilistically regularize the global model and preserve past knowledge. Experimental evaluations show that our method achieves stable convergence and significantly outperforms state-of-the-art methods on standard benchmarks—for example, it improves accuracy by 5.27% over FedCIL on EMNIST-Balanced and by 1.60% and 1.27% over the strongest baselines on TinyImageNet and ImageNet-LT, respectively—while maintaining a forgetting rate below 5% across tasks. By jointly addressing feature drift and catastrophic forgetting, RAVEL-FCL provides a robust and privacy-preserving solution suitable for the adaptive and autonomous systems envisioned for Industry 5.0.