  Many large-scale machine learning (ML) applications need to perform
decentralized learning over datasets generated at different devices and
locations. Such datasets pose a significant challenge to decentralized learning
because their different contexts result in significant data distribution skew
across devices/locations. In this paper, we take a step toward better
understanding this challenge by presenting a detailed experimental study of
decentralized DNN training on a common type of data skew: skewed distribution
of data labels across devices/locations. Our study shows that: (i) skewed data
labels are a fundamental and pervasive problem for decentralized learning,
causing significant accuracy loss across many ML applications, DNN models,
training datasets, and decentralized learning algorithms; (ii) the problem is
particularly challenging for DNN models with batch normalization; and (iii) the
degree of data skew is a key determinant of the difficulty of the problem.
Based on these findings, we present SkewScout, a system-level approach that
adapts the communication frequency of decentralized learning algorithms to the
(skew-induced) accuracy loss between data partitions. We also show that group
normalization can recover much of the accuracy loss of batch normalization.
