11th International Conference on the Internet of Things

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 m...

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Detalles Bibliográficos
Autor principal: Association for Computing Machinery (author)
Autor Corporativo: Association for Computing Machinery, author, issuing body (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: New York : Association for Computing Machinery 2021.
Colección:ACM Other conferences
Materias:
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009714275306719
Descripción
Sumario: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.
Descripción Física:1 online resource (233 pages) : illustrations