Nazreen Shah

Federated Continual Learning as a Distributed Drift-Plus-Penalty Control Problem

This work casts FCL as a stochastic control problem and proposes Federated Queue-regulated Continual Learning (FedQCL), a framework based on Lyapunov drift-plus-penalty (DPP) optimization that outperforms state-of-the-art baselines with respect to accuracy while significantly reducing forgetting under heterogeneous data distributions.