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Advances in Neural Information Processing Systems , volume=

A unified analysis of federated learning with arbitrary client participation , author=

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FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training

cs.DC · 2026-05-04 · unverdicted · novelty 6.0

FedQueue predicts per-facility queue delays, applies cutoff admission to bound staleness, and uses staleness-aware aggregation, yielding O(1/sqrt(R)) convergence for non-convex objectives and up to 60% faster time-to-target-accuracy in simulations and 20.5% real-world improvement.

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  • FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training cs.DC · 2026-05-04 · unverdicted · none · ref 24

    FedQueue predicts per-facility queue delays, applies cutoff admission to bound staleness, and uses staleness-aware aggregation, yielding O(1/sqrt(R)) convergence for non-convex objectives and up to 60% faster time-to-target-accuracy in simulations and 20.5% real-world improvement.