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, up to 60% reduction in time to reach a target accuracy level under high queue variance and non-IID partitions.

翻译：联邦学习（FL）在跨多个高性能计算设施中面临批处理调度器带来的随机准入延迟，该延迟主导了挂钟时间。同步FL受严重掉队者问题困扰，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，其将调度器延迟直接纳入训练与聚合过程，具体而言：(i) 在线预测每个设施的队列延迟以预算本地工作量，(ii) 采用基于截止值的准入机制缓冲延迟到达者以限制过时性，(iii) 执行过时感知的聚合以稳定异构本地工作负载。我们证明了在有限过时性条件下非凸目标以$\mathcal{O}(1/\sqrt{R})$速率收敛，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性。FedQueue在实际跨设施部署中相较基线算法提升20.5%性能。受控队列模拟实验表明其对基线算法具有稳健改进；特别是在队列高度波动和非独立同分布分区条件下，达到目标精度水平的时间最多可减少60%。