Federated Continual Learning as a Distributed Drift-Plus-Penalty Control Problem
Federated Continual Learning (FCL) is reformulated as a stochastic control problem rather than a sequence of per-task optimization problems The proposed FedQCL framework uses Lyapunov drift-plus-penalty (DPP) optimization with virtual queues to explicitly track and regulate forgetting across tasks and clients A single tunable parameter controls the stability-plasticity trade-off, eliminating the need for gradient projection or extra communication overhead FedQCL outperforms state-of-the-art base
Analysis
TL;DR
- Federated Continual Learning (FCL) is reformulated as a stochastic control problem rather than a sequence of per-task optimization problems
- The proposed FedQCL framework uses Lyapunov drift-plus-penalty (DPP) optimization with virtual queues to explicitly track and regulate forgetting across tasks and clients
- A single tunable parameter controls the stability-plasticity trade-off, eliminating the need for gradient projection or extra communication overhead
- FedQCL outperforms state-of-the-art baselines on Split-CIFAR-10, Split-CIFAR-100, and Split-TinyImageNet in both accuracy and forgetting reduction under non-IID data distributions
- Forgetting in FCL is characterized as a long-term, distributed phenomenon arising from the interaction of temporal task evolution and cross-client heterogeneity
Why It Matters
This work bridges control theory and federated continual learning, offering a principled mathematical framework rather than heuristic solutions to catastrophic forgetting in distributed settings. For AI practitioners building systems that must continuously learn from decentralized, heterogeneous data streams, FedQCL provides a theoretically grounded approach with minimal overhead. The single-parameter tunability makes it particularly attractive for production deployment where interpretability and ease of configuration are critical.
Technical Details
- Core Framework: FedQCL (Federated Queue-regulated Continual Learning) casts FCL as a stochastic control problem using Lyapunov drift-plus-penalty optimization, introducing virtual queues that accumulate forgetting signals across both time (tasks) and space (clients)
- Mechanism: The virtual queue formulation creates an explicit, interpretable control loop for the stability-plasticity dilemma — queues grow when forgetting occurs and shrink when knowledge is retained, with the DPP objective jointly optimizing current-task accuracy and queue stability
- Key Innovation: Unlike prior methods relying on replay buffers, regularization penalties, or gradient projection, FedQCL achieves forgetting control through a single tuning parameter without additional communication overhead or architectural modifications
- Benchmarks: Evaluated on Split-CIFAR-10, Split-CIFAR-100, and Split-TinyImageNet under non-IID data distributions, demonstrating superior accuracy and significantly reduced forgetting compared to state-of-the-art baselines
- Theoretical Foundation: Leverages Lyapunov optimization theory to provide stability guarantees, treating cross-client heterogeneity and temporal task drift as coupled dynamics within a distributed control framework
Industry Insight
- The control-theoretic framing of continual learning opens a new research direction where techniques from distributed systems and network optimization can directly inform federated learning design, potentially accelerating progress in both fields
- The single-parameter tunability and zero additional communication overhead make FedQCL highly deployable in resource-constrained edge environments where existing replay or regularization-based methods incur prohibitive costs
- As real-world federated systems increasingly encounter non-stationary data distributions across heterogeneous clients, queue-based forgetting regulation may become a standard component in production continual learning pipelines, particularly for IoT, healthcare, and mobile applications
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