Distributed Deep Learning using Stochastic Gradient Staleness

• URL: http://arxiv.org/abs/2509.05679v1
• Date: Sat, 06 Sep 2025 11:05:40 GMT
• Title: Distributed Deep Learning using Stochastic Gradient Staleness
• Authors: Viet Hoang Pham, Hyo-Sung Ahn,
• Abstract summary: High performing deep neural networks (DNNs) tend to become increasingly deep and require extensive training datasets.<n>This paper introduces a distributed training method that integrates data parallelism and fully decoupled parallel backpropagation algorithm.
• Score: 4.254099382808598
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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