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

2026-08-21

AI Summary

本文针对联邦持续学习中的灾难性遗忘和客户端漂移问题，提出了一种基于Lyapunov漂移加惩罚优化的方法FedQCL，通过引入虚拟队列来平衡模型的稳定性和可塑性。

Abstract

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, we cast FCL as a stochastic control problem and propose Federated Queue-regulated Continual Learning (FedQCL), a framework based on Lyapunov drift-plus-penalty (DPP) optimization. FedQCL introduces virtual queues to track the accumulation of forgetting across tasks and clients, enabling explicit control of the stability-plasticity trade-off. By optimizing a DPP objective, the method jointly improves current-task performance while the queue-based formulation provides an interpretable and tunable mechanism to balance adaptation and retention through a single parameter, without requiring gradient projection or additional communication overhead. Empirical evaluations on standard benchmarks, including Split-CIFAR-10, Split-CIFAR-100, and Split-TinyImageNet, demonstrate that FedQCL outperforms state-of-the-art baselines with respect to accuracy while significantly reducing forgetting under heterogeneous data distributions.

Problem

• Federated Continual Learning
• Catastrophic Forgetting
• Client Drift
• Distributed Learning
• Non-IID Data

Innovation

• Federated Queue-regulated Continual Learning
• Lyapunov Drift-Plus-Penalty Optimization
• Virtual Queues
• Stability-Plasticity Trade-off
• Heterogeneous Data Distributions