  Distributed asynchronous offline training has received widespread attention
in recent years because of its high performance on large-scale data and complex
models. As data are distributed from cloud-centric to edge nodes, a big
challenge for distributed machine learning systems is how to handle native and
natural non-independent and identically distributed (non-IID) data for
training. Previous asynchronous training methods do not have a satisfying
performance on non-IID data because it would result in that the training
process fluctuates greatly which leads to an abnormal convergence. We propose a
gradient scheduling algorithm with partly averaged gradients and global
momentum (GSGM) for non-IID data distributed asynchronous training. Our key
idea is to apply global momentum and local average to the biased gradient after
scheduling, in order to make the training process steady. Experimental results
show that for non-IID data training under the same experimental conditions,
GSGM on popular optimization algorithms can achieve a 20% increase in training
stability with a slight improvement in accuracy on Fashion-Mnist and CIFAR-10
datasets. Meanwhile, when expanding distributed scale on CIFAR-100 dataset that
results in sparse data distribution, GSGM can perform a 37% improvement on
training stability. Moreover, only GSGM can converge well when the number of
computing nodes grows to 30, compared to the state-of-the-art distributed
asynchronous algorithms. At the same time, GSGM is robust to different degrees
of non-IID data.
