Towards Geo-Distributed Machine Learning.
Ignacio Cano, Markus Weimer, Dhruv Kumar Mahajan, Carlo Curino, Giovanni Matteo Fumarola · IEEE Data(base) Engineering Bulletin · 2016
In large organizations, data is “born” in data centers all around the world. Learning requires a global view of such data. This new class of geo-distributed machine learning (GDML) applications need to cope with: 1) scarce and expensive cross-data center bandwidth, and 2) growing privacy concerns that are pushing for stricter data sovereignty regulations. In this paper, we formalize this problem, show that the current state-of-the-art lacks proper support for GDML applications, and propose an initial system and algorithm that perform training in a geo-distributed fashion. Our empirical evaluation confirms the general validity of our approach, but many research challenges remain open.