On-device Federated Learning with Flower

• URL: http://arxiv.org/abs/2104.03042v1
• Date: Wed, 7 Apr 2021 10:42:14 GMT
• Title: On-device Federated Learning with Flower
• Authors: Akhil Mathur, Daniel J. Beutel, Pedro Porto Buarque de Gusm\~ao, Javier Fernandez-Marques, Taner Topal, Xinchi Qiu, Titouan Parcollet, Yan Gao, Nicholas D. Lane
• Abstract summary: Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device. Despite the algorithmic advancements in FL, the support for on-device training of FL algorithms on edge devices remains poor. We present an exploration of on-device FL on various smartphones and embedded devices using the Flower framework.
• Score: 22.719117235237036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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