Federated Continual Learning as a Distributed Drift-Plus-Penalty Control Problem
Federated Continual Learning (FCL) is fundamental to real-world distributed learning systems, requiring models to adapt to sequential, non-IID data across clients while mitigating catastrophic forgetting and client drift. Existing approaches formulate continual learning (CL) as a sequence of per-task optimization problems, applied locally at each client and coupled through aggregation, using heuristic mechanisms such as replay, regularization, or projection-based constraints. However, forgetting in FCL is inherently a long-term, distributed phenomenon, arising from the interaction of temporal task evolution and cross-client heterogeneity, which is not explicitly regulated. In this work, w...
| Authors | Nazreen Shah, Naveen Kumar Reddy Somireddy, Zubair Shaban et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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