DBLP: Phase-Aware Bounded-Loss Transport for Burst-Resilient Distributed ML Training
Distributed machine learning (ML) training has become a necessity with the prevalence of billion to trillion-parameter-scale models. While prior work has improved training efficiency from the ML perspective at the application layer, it often fails to address transient congestion events at the network layer that introduce severe tail latency and training-time variability, thereby undermining the quality of service of distributed ML training systems. Existing network optimizations treat all gradients equally and thus fail to integrate sufficient model-training insights into communication protocol design. In this paper, we present Dynamic Bounded-Loss Protocol (DBLP), a burst-resilient, trai...
| Authors | Zechen Ma, Zixi Qu, Jinyan Yi et al. |
|---|---|
| Primary category | cs.LG |
| Published | 3 May 2026 |
| Last updated at source | 4 July 2026 |
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