# Distributed Deep Learning using Stochastic Gradient Staleness

arXiv: https://arxiv.org/abs/2509.05679 · HTML: https://mlsanity.com/paper/2509.05679/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 6 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [Optimization Methods for Large-Scale Machine Learning](https://mlsanity.com/paper/1606.04838/) (2016)
- [Stochastic Gradient Push for Distributed Deep Learning](https://mlsanity.com/paper/1811.10792/) (2018)
- [Decoupled Neural Interfaces using Synthetic Gradients](https://mlsanity.com/paper/1608.05343/) (2016)
- [Distributed Deep Learning in Open Collaborations](https://mlsanity.com/paper/2106.10207/) (2021)
- [A Review of Deep Learning Techniques for Speech Processing](https://mlsanity.com/paper/2305.00359/) (2023)
- [Decoupled Parallel Backpropagation with Convergence Guarantee](https://mlsanity.com/paper/1804.10574/) (2018)
- [torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models](https://mlsanity.com/paper/2004.09910/) (2020)
- [HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array](https://mlsanity.com/paper/1901.02067/) (2019)
- [Fully Decoupled Neural Network Learning Using Delayed Gradients](https://mlsanity.com/paper/1906.09108/) (2019)
- [Hybrid Data-Model Parallel Training for Sequence-to-Sequence Recurrent Neural Network Machine Translation](https://mlsanity.com/paper/1909.00562/) (2019)

## Abstract (arXiv)

Despite the notable success of deep neural networks (DNNs) in solving complex tasks, the training process still remains considerable challenges. A primary obstacle is the substantial time required for training, particularly as high performing DNNs tend to become increasingly deep (characterized by a larger number of hidden layers) and require extensive training datasets. To address these challenges, this paper introduces a distributed training method that integrates two prominent strategies for accelerating deep learning: data parallelism and fully decoupled parallel backpropagation algorithm. By utilizing multiple computational units operating in parallel, the proposed approach enhances the amount of training data processed in each iteration while mitigating locking issues commonly associated with the backpropagation algorithm. These features collectively contribute to significant improvements in training efficiency. The proposed distributed training method is rigorously proven to converge to critical points under certain conditions. Its effectiveness is further demonstrated through empirical evaluations, wherein an DNN is trained to perform classification tasks on the CIFAR-10 dataset.