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.
@article{arxiv.2104.03042,
title = {On-device Federated Learning with Flower},
author = {Akhil Mathur and Daniel J. Beutel and Pedro Porto Buarque de Gusmão and Javier Fernandez-Marques and Taner Topal and Xinchi Qiu and Titouan Parcollet and Yan Gao and Nicholas D. Lane},
journal= {arXiv preprint arXiv:2104.03042},
year = {2021}
}
Comments
Accepted at the 2nd On-device Intelligence Workshop @ MLSys 2021. arXiv admin note: substantial text overlap with arXiv:2007.14390