Research map / Optimization / Distributed and compressed
Sequential Gradient Coding For Straggler Mitigation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use M-SGC instead of GC | distributed gradient computation · MNIST, 4 concurrent CNN classifiers (AWS Lambda) | run time: 1064.96 ± 46.72 -> 891.37 ± 43.10; 16% lower run time | Helped |
| Use SR-SGC instead of GC | distributed gradient computation · MNIST, 4 concurrent CNN classifiers (AWS Lambda) | run time: 1064.96 ± 46.72 -> 994.22 ± 43.66 | Helped |
| Use coding (GC) instead of no coding | distributed gradient computation · MNIST, 4 concurrent CNN classifiers (AWS Lambda) | run time: 1307.79 ± 61.88 -> 1064.96 ± 46.72 | Helped |
Closest papers by meaning
- Leveraging partial stragglers within gradient coding (NeurIPS 2024 · similarity 0.91)
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- Approximate Gradient Coding for Distributed Learning with Heterogeneous Stragglers (NeurIPS 2025 · similarity 0.90)
- Gradient Coding from Cyclic MDS Codes and Expander Graphs (ICML 2018 · similarity 0.90)
- Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework (NeurIPS 2024 · similarity 0.85)
- Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity (NeurIPS 2026 · similarity 0.84)
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