OCS-BAHFL: Online Communication Scheduling for Buffered Asynchronous Hierarchical Federated Learning

Buffered asynchronous hierarchical federated learning (HFL) reduces synchronization overhead, but introduces a fundamental communication-timing problem: aggregating too frequently wastes communication and may rely on weak evidence, whereas waiting too long increases information age and staleness. We propose OCS-BAHFL, an online communication scheduling framework that independently adapts communication timing at the client–edge and edge–cloud levels. OCS-BAHFL controls an edge holding time and a cloud aggregation deadline using only event-local communication statistics, including effective evidence, event frequency, information age, and model disagreement. These observations are converted into self-normalized accumulation and freshness pressures, whose balance defines a strictly convex scalar surrogate with a closed-form waiting-time target. A projected log-domain update tracks this target while preserving positive and bounded communication timers. The scheduler requires no validation accuracy, training-loss feedback, or additional communication messages. We further establish bounded communication-induced delay, characterize controller tracking, and connect effective evidence and bounded hierarchical staleness to convergence under smooth non-convex objectives. Experiments on non-IID MNIST demonstrate that OCS-BAHFL dynamically adjusts communication timing under different local-training budgets and reduces communication volume relative to hierarchical FedAvg and a non-adaptive configuration. In particular, at the largest evaluated local-training budget, OCS-BAHFL reduces communication volume relative to hierarchical FedAvg while maintaining competitive learning performance. These results show that communication timing itself can serve as an effective online control variable in buffered asynchronous HFL.