The Non-IID Data Quagmire of Decentralized Machine Learning

Understand

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.