Many of today's parallel machine learning algorithms were developed for tightly coupled systems like computing clusters or clouds. However, the volumes of data generated from machine-to-machine interaction, by mobile phones or autonomous vehicles, surpass the amount of data that can be realistically centralized. Thus, traditional cloud computing approaches are rendered infeasible.
To scale parallel machine learning to such volumes of data, computation needs to be pushed towards the edge, that is, towards the data generating devices. By learning models directly on the data sources - which often have computational power of their own, for example, mobile phones, smart sensors, and tablets - network communication can be reduced by orders of magnitude. Moreover, it enables training a central model without centralizing privacy-sensitive data. This workshop aims to foster discussion, discovery, and dissemination of novel ideas and approaches for decentralized machine learning.