# 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/en/paper/1902.01046v1/

## Key points

- The paper "Towards Federated Learning at Scale: System Design" discusses a scalable production system for Federated Learning on mobile devices using TensorFlow.
- Federated Learning is a distributed machine learning approach that allows model training on decentralized data.
- The authors provide solutions to challenges encountered during the development of their system and discuss open problems and potential future directions for Federated Learning.
- This paper offers valuable insights into building scalable production systems for Federated Learning.

## Summary

The paper "Towards Federated Learning at Scale: System Design" describes a scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow. The 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 and Jason Roselander - explain that Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. The authors outline the resulting high-level design of their system and provide solutions to some of the challenges they encountered during its development. They also discuss open problems and potential future directions for Federated Learning. This paper provides valuable insights into how to build scalable production systems for Federated Learning on mobile devices using TensorFlow.