Research Markets

Variance Reduction for Decentralized Stochastic Optimization with Multiple Local Updates

Oct 30, 2026 Source ↗

Key Insights

Multiple local updates are widely used in decentralized optimization to enhance communication efficiency, often in conjunction with stochastic gradients when exact gradient computation is costly.
Howe.

Cite this synthesis DOI: 10.48550/arXiv.2610.41927 ↗

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@misc{ acaciamarkets-research-variance-reduction-for-decentralized-stochastic-optimization,
  title = { Variance Reduction for Decentralized Stochastic Optimization with Multiple Local Updates },
  author = { Leszek },
  year = { 2026 },
  doi = { 10.48550/arXiv.2610.41927 },
  url = { http://arxiv.org/abs/2610.41927v1 },
  note = {Summarized and classified by AcaciaFund}
}

TY  - GEN
TI  - Variance Reduction for Decentralized Stochastic Optimization with Multiple Local Updates
AU  - Leszek
PY  - 2026
DO  - 10.48550/arXiv.2610.41927
UR  - http://arxiv.org/abs/2610.41927v1
ER  -

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Overview

Multiple local updates are widely used in decentralized optimization to enhance communication efficiency, often in conjunction with stochastic gradients when exact gradient computation is costly. Howe

Key Topics

Quantitative Modeling: A theme covered by this item within the markets context.

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Evergreen Synthesized

market-microstructure quantitative-modeling

Market Microstructure

SQI 0.750