On-device Federated Learning with Flower

Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store data in the cloud. Despite the algorithmic advancements in FL, the support for...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-04
Main Authors: Mathur, Akhil, Beutel, Daniel J, Pedro Porto Buarque de Gusmão, Fernandez-Marques, Javier, Topal, Taner, Qiu, Xinchi, Parcollet, Titouan, Gao, Yan, Lane, Nicholas D
Format: Article
Language:English
Subjects:
Algorithms
Decoupling
Electronic devices
Federated learning
Machine learning
Prediction models
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store data in the cloud. Despite the algorithmic advancements in FL, the support for on-device training of FL algorithms on edge devices remains poor. In this paper, we present an exploration of on-device FL on various smartphones and embedded devices using the Flower framework. We also evaluate the system costs of on-device FL and discuss how this quantification could be used to design more efficient FL algorithms.
ISSN:2331-8422