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181   $ctxt$2rdacontent
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200 10$a11th International Conference on the Internet of Things /$fAssociation for Computing Machinery
210  1$aNew York :$cAssociation for Computing Machinery,$d2021.
215   $a1 online resource (233 pages) $cillustrations
311   $a1-4503-8566-4 
330   $aThe 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.
606   $aInternet of things$vCongresses
606   $aArtificial intelligence$vCongresses
615  0$aInternet of things
615  0$aArtificial intelligence
676   $a004
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801  1$bNjHacl
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912   $a9910553101203321
996   $a11th International Conference on the Internet of Things$92811309
997   $aUNINA