Poster: Testbed in Wireless City Mesh Network with Application to Federated Learning Experiments
Poster: Testbed in Wireless City Mesh Network with Application to Federated Learning Experiments
复制标题
海报：无线城市网状网络测试平台及其在联邦学习实验中的应用
DOI：
10.1145/3494322.3494353
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
Selimi, Mennan
中科院分区：
文献类型：
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作者：
Freitag, Felix;Vilchez, Pedro;Wei, Lu;Liu, Chung-Hung;Selimi, Mennan
物联网设备计算能力的提高和轻量级机器学习框架的出现，使得机器学习现在可以在网络边缘的物联网应用中运行。有机会通过越来越强大的计算能力边缘节点来实施机器学习算法，并使用来自附近传感器的不断增加的本地数据量。为此，联邦学习成为一种有前途的机器学习方法，其中机器学习模型由各个节点使用其本地数据进行训练。为了执行实际的联合学习实验，我们构建了一个测试台，部署在无线城市网状网络中，并具有地理上分布的低容量设备。我们描述了测试床的实现，并展示了其在真实边缘环境中通过实验研究联邦学习协议和算法的潜力。
The increase of the computing capacity of IoT devices and the appearance of lightweight machine learning frameworks have led to the situation that machine learning can nowadays be run in IoT applications at the network edge. There is an opportunity to implement machine learning algorithms with the more and more computationally powerful edge nodes and using the ever increasing amount of local data coming from nearby sensors. For this purpose, federated learning becomes a promising machine learning approach, where a machine learning model is trained by various nodes using their local data. For performing practical federated learning experiments, we have built a testbed deployed within a wireless city mesh network with geographically distributed low capacity devices. We describe the testbed implementation and show its potential to experimentally study federated learning protocols and algorithms in real edge environments.
DOI：
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发表时间：
2013
期刊：
International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems
影响因子：
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作者：
Llorenç Cerdà;Axel Neumann;P. Garcia
通讯作者：
P. Garcia
Guifi.net，一个共同的众包网络基础设施
DOI：
10.1016/j.comnet.2015.07.009
发表时间：
2015
期刊：
Comput. Networks
影响因子：
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作者：
Roger Baig;Ramon Roca;Felix Freitag;L. Navarro
通讯作者：
L. Navarro