FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training
For federated learning across HPC facilities, this work provides a practical solution to scheduler-induced delays, a known bottleneck in cross-site training.
FedQueue addresses stochastic admission delays in cross-facility HPC federated learning, achieving 20.5% improvement over baselines in real-world deployment and 34% reduction in time to target accuracy under high queue variance and non-IID data.
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.