  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.
