# Towards Federated Learning at Scale: System Design

**Authors:** Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, H. Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, Jason Roselander  
**arXiv:** [1902.01046](https://arxiv.org/abs/1902.01046)  
**Date:** 2019-02-04  
**Summary page:** https://www.summarizepaper.com/fr/paper/1902.01046v1/

## Abstract

Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions.