Towards Federated Learning at Scale: System Design

TL;DR

This paper presents a scalable system for federated learning on mobile devices using TensorFlow, addressing design challenges and outlining future research directions.

Contribution

It introduces a high-level system design for large-scale federated learning on mobile devices, highlighting solutions to key challenges.

Findings

Developed a scalable federated learning system for mobile devices

Identified and addressed major system design challenges

Outlined open problems and future research directions

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.