Demo: An Experimental Environment Based On Mini-PCs For Federated Learning Research
Demo: An Experimental Environment Based On Mini-PCs For Federated Learning Research
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DOI：
10.1109/ccnc49033.2022.9700579
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Felix Freitag;Pedro Vilchez;Lu Wei;Chun-Hung Liu;Mennan Selimi;I. Koutsopoulos
中科院分区：
文献类型：
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作者：
Felix Freitag;Pedro Vilchez;Lu Wei;Chun-Hung Liu;Mennan Selimi;I. Koutsopoulos
联邦学习（FL）是一种很有前途的数据隐私保护方法，并且可以将训练接近生成数据的网络边缘。机器学习（ML）训练和推理的资源消耗对边缘节点很重要，但大多数针对FL提出的协议和算法都是通过模拟来评估的。在这篇演示论文中，我们提出了一个基于分布式微型PC的环境，使FL协议和算法的实验研究。我们已经在无线城市级网状网络中安装了低容量的迷你PC，并在这些节点上部署了基于容器的FL组件。我们展示了部署在城市不同节点的FL客户端和服务器，并演示了如何在真实的环境中设置和运行FL实验。
There is a growing research interest in Federated Learning (FL), a promising approach for data privacy preservation and proximity of training to the network edge, where data is generated. Resource consumption for Machine Learning (ML) training and inference is important for edge nodes, but most of the proposed protocols and algorithms for FL are evaluated by simulations. In this demo paper, we present an environment based on distributed mini-PCs to enable experimental study of FL protocols and algorithms. We have installed low-capacity mini-PCs within a wireless city-level mesh network and deployed container-based FL components on these nodes. We show the deployed FL clients and server at different nodes in the city and demonstrate how an FL experiment can be set and run in a real environment.