LOSCAR-SGD combines local updates, sparse model averaging, and communication-computation overlap with a delay-corrected merge rule, providing convergence rates for smooth non-convex objectives under worker heterogeneity.
Journal of Machine Learning Research , volume=
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Ringmaster LMO extends delay-thresholding from ASGD to LMO-based momentum updates, providing convergence guarantees under (L0, L1)-smoothness and time-complexity bounds that recover optimal rates in the Euclidean case.
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.
Rennala MVR improves time complexity over Rennala SGD for smooth nonconvex stochastic optimization in heterogeneous parallel systems under a mean-squared smoothness assumption.
citing papers explorer
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LOSCAR-SGD: Local SGD with Communication-Computation Overlap and Delay-Corrected Sparse Model Averaging
LOSCAR-SGD combines local updates, sparse model averaging, and communication-computation overlap with a delay-corrected merge rule, providing convergence rates for smooth non-convex objectives under worker heterogeneity.
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Ringmaster LMO: Asynchronous Linear Minimization Oracle Momentum Method
Ringmaster LMO extends delay-thresholding from ASGD to LMO-based momentum updates, providing convergence guarantees under (L0, L1)-smoothness and time-complexity bounds that recover optimal rates in the Euclidean case.
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FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training
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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Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction
Rennala MVR improves time complexity over Rennala SGD for smooth nonconvex stochastic optimization in heterogeneous parallel systems under a mean-squared smoothness assumption.