AET-SGD: Asynchronous Event-triggered Stochastic Gradient Descent

• URL: http://arxiv.org/abs/2112.13935v1
• Date: Mon, 27 Dec 2021 23:20:04 GMT
• Title: AET-SGD: Asynchronous Event-triggered Stochastic Gradient Descent
• Authors: Nhuong Nguyen, Song Han
• Abstract summary: Communication cost is the main bottleneck for the design of effective distributed learning algorithms. We propose a Asynchronous Event-triggered Gradient Descent (SGD) framework, called AET-SGD, to reduce the communication cost among the compute nodes. We show that AET-SGD can resist large delay from the straggler nodes while obtaining a decent performance and a desired speedup ratio.
• Score: 10.029039979947798
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Communication cost is the main bottleneck for the design of effective distributed learning algorithms. Recently, event-triggered techniques have been proposed to reduce the exchanged information among compute nodes and thus alleviate the communication cost. However, most existing event-triggered approaches only consider heuristic event-triggered thresholds. They also ignore the impact of computation and network delay, which play an important role on the training performance. In this paper, we propose an Asynchronous Event-triggered Stochastic Gradient Descent (SGD) framework, called AET-SGD, to i) reduce the communication cost among the compute nodes, and ii) mitigate the impact of the delay. Compared with baseline event-triggered methods, AET-SGD employs a linear increasing sample size event-triggered threshold, and can significantly reduce the communication cost while keeping good convergence performance. We implement AET-SGD and evaluate its performance on multiple representative data sets, including MNIST, FashionMNIST, KMNIST and CIFAR10. The experimental results validate the correctness of the design and show a significant communication cost reduction from 44x to 120x, compared to the state of the art. Our results also show that AET-SGD can resist large delay from the straggler nodes while obtaining a decent performance and a desired speedup ratio.

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