02021nam 2200349 450 991055310120332120230829114717.0(CKB)5580000000296232(NjHacI)995580000000296232(EXLCZ)99558000000029623220230829d2021 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrier11th International Conference on the Internet of Things /Association for Computing MachineryNew York :Association for Computing Machinery,2021.1 online resource (233 pages) illustrations1-4503-8566-4 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.Internet of thingsCongressesArtificial intelligenceCongressesInternet of thingsArtificial intelligence004NjHacINjHaclBOOK991055310120332111th International Conference on the Internet of Things2811309UNINA