Federated learning (FL) across multiple HPC facilities faces stochastic admission delays from batch schedulers that dominate wall-clock time. Synchronous FL suffers from severe stragglers, while asynchronous FL accumulates stale updates when queues spike. We propose FedQueue, a queue-aware FL protocol that incorporates scheduler delays directly into training and aggregation, which (i) predicts per-facility queue delays online to budget local work, (ii) applies cutoff-based admission that buffers late arrivals to bound staleness, and (iii) performs staleness-aware aggregation to stabilize heterogeneous local workloads. We prove the convergence for non-convex objectives at rate $\mathcal{O}(1/\sqrt{R})$ under bounded staleness, and show that the admission controls yield bounded staleness with high probability under queue-prediction error. Real-world cross-facility deployment of FedQueue shows 20.5% improvement over baseline algorithms. Controlled queue simulations demonstrate robust improvement over the baselines; in particular, about 34% reduction in time to reach a target accuracy level under high queue variance and non-IID partitions.

翻译：联邦学习（FL）在跨多个HPC设施中面临来自批处理调度器的随机准入延迟，该延迟主导了挂壁时间。同步FL受严重的掉队者问题影响，而异步FL在队列激增时会累积过时更新。我们提出FedQueue，一种队列感知的联邦学习协议，将调度器延迟直接纳入训练与聚合过程，具体包括：(i)在线预测每个设施的队列延迟以预算本地工作量，(ii)应用基于截断的准入机制，缓冲迟到的抵达以限制过时程度，(iii)执行过时感知的聚合以稳定异构本地工作负载。我们证明了非凸目标函数在有限过时条件下的收敛率为$\mathcal{O}(1/\sqrt{R})$，并表明在队列预测误差下，准入控制能以高概率保证有限过时。FedQueue在真实跨设施部署中较基线算法提升20.5%。受控队列仿真表明其相较于基线具有稳健改进；特别是在高队列方差与非独立同分布划分条件下，达到目标精度水平的时间减少约34%。