pith:MB47VM45
Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
Rescaling worker stepsizes by computation time fixes bias in asynchronous SGD so it converges to the true global objective.
arxiv:2605.13434 v1 · 2026-05-13 · cs.LG · cs.DC · math.OC · stat.ML
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Claims
we prove that the resulting method, Rescaled ASGD, converges to stationary points of the correct global objective in the fixed-computation model. Its time complexity matches the known lower bound in the leading term, while the effects of staleness and data heterogeneity appear only in lower-order terms.
under smoothness and bounded heterogeneity assumptions
Rescaled ASGD recovers convergence to the true global objective by rescaling worker stepsizes proportional to computation times, matching the known time lower bound in the leading term under non-convex smoothness and bounded heterogeneity.
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| First computed | 2026-05-18T02:44:47.131927Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
6079fab39dc3866ae6040e29fb008cdcddec03046b3a673109882c1096967fa9
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/MB47VM45YODGVZQEBYU7WAEM3T \
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Canonical record JSON
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