摘要:针对用户移动性和设备异构的联邦学习的硬件原型

Allen-Jasmin Farcas, R. Marculescu
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引用次数: 0

摘要

本文提出了一个新的硬件原型,用于探索集中式和分层式联邦学习系统如何受到现实世界设备分布、可用性和异构性的影响。我们的结果表明,当考虑到用户的移动性时,在训练过程中会出现相当大的学习性能下降和能量浪费。因此,我们提供了一个原型,可以用作设计探索工具,以更好地设计、校准和评估真实部署的FL系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Demo Abstract: A Hardware Prototype Targeting Federated Learning with User Mobility and Device Heterogeneity
This paper presents a new hardware prototype to explore how centralized and hierarchical federated learning systems are impacted by real-world devices distribution, availability, and heterogeneity. Our results show considerable learning performance degradation and wasted energy during training when users mobility is accounted for. Hence, we provide a prototype that can be used as a design exploration tool to better design, calibrate and evaluate FL systems for real-world deployment.
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