Pipelined Gradient Coding
In large-scale machine learning, distributed training commonly involves multiple workers evaluating the gradients of the model on different dataset partitions. A common challenge is the presence of straggling workers, which may significantly slow down training. Traditional gradient coding (GC) addresses this by duplicating dataset partitions across workers, allowing for the replacement of missing gradients from stragglers. However, GC requires workers to evaluate gradients on multiple dataset partitions in each step, potentially increasing overall training time. In this paper, we propose to pipeline GC, such that gradient evaluation is segmented across multiple steps and each worker evalu...
| Authors | Xian Su, Jun Li |
|---|---|
| Primary category | cs.IT |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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