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Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity

Ammar Mahran, Artavazd Maranjyan, Peter Richt\'arik

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

C1strongest claim

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.

C2weakest assumption

under smoothness and bounded heterogeneity assumptions

C3one line summary

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.

References

300 extracted · 300 resolved · 34 Pith anchors

[1] Federated Learning: Chal- lenges, Methods, and Future Directions.IEEE Signal Processing Magazine, 37(3):50–60 2020 · doi:10.1109/msp.2020.2975749
[2] Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification 1909 · doi:10.48550/arxiv.1909.06335
[3] arXiv.org , author = 2015 · doi:10.1109/tit.2017.2736066
[4] Leaf: A benchmark for federated settings · doi:10.48550/arxiv.1812.01097
[5] arXiv.org , author = 2012

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First computed 2026-05-18T02:44:47.131927Z
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6079fab39dc3866ae6040e29fb008cdcddec03046b3a673109882c1096967fa9

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arxiv: 2605.13434 · arxiv_version: 2605.13434v1 · doi: 10.48550/arxiv.2605.13434 · pith_short_12: MB47VM45YODG · pith_short_16: MB47VM45YODGVZQE · pith_short_8: MB47VM45
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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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