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On-device training of machine learning models on microcontrollers with a look at federated learning

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10.1145/3462203.3475896
 
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hdl:2117/356500

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Monfort Grau, Marc
Pueyo Centelles, RogerMés informacióMés informació
Freitag, FèlixMés informacióMés informacióMés informació
Document typeConference report
Defense date2021
PublisherAssociation for Computing Machinery (ACM)
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
ProjectSISTEMAS INFORMATICOS Y DE RED DESCENTRALIZADOS CON RECURSOS DISTRIBUIDOS (AEI-PID2019-106774RB-C21)
PROCESAMIENTO DE FLUJO DISTRIBUIDO EN SISTEMAS DE NIEBLA Y BORDE MEDIANTE COMPUTACION TRANSPRECISA (AEI-PCI2019-111850-2)
MARCO DE ASIGNACION DE RECURSOS HOLISTICO Y FUNDACIONAL PARA SERVICIOS EDGE COMPUTING OPTIMIZADOS Y CON ALTO IMPACTO (AEI-PCI2019-111851-2)
Abstract
Recent progress in machine learning frameworks makes it now possible to run an inference with sophisticated machine learning models on tiny microcontrollers. Model training, however, is typically done separately on powerful computers. There, the training process has abundant CPU and memory resources to process the stored datasets. In this work, we explore a different approach: training the model directly on the microcontroller. We implement this approach for a keyword spotting task. Then, we extend the training process using federated learning among microcontrollers. Our experiments with model training show an overall trend of decreasing loss with the increase of training epochs.
CitationMonfort, M.; Pueyo, R.; Freitag, F. On-device training of machine learning models on microcontrollers with a look at federated learning. A: ACM International Conference on Information Technology for Social Good. "GoodIT'21: proceedings of the 2021 Conference on Information Technology for Social Good: September 9–11, 2021, Roma, Italy". New York: Association for Computing Machinery (ACM), 2021, p. 198-203. ISBN 978-1-4503-8478-0. DOI 10.1145/3462203.3475896. 
URIhttp://hdl.handle.net/2117/356500
DOI10.1145/3462203.3475896
ISBN978-1-4503-8478-0
Publisher versionhttps://dl.acm.org/doi/10.1145/3462203.3475896
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  • Doctorat en Arquitectura de Computadors - Ponències/Comunicacions de congressos [251]
  • CNDS - Xarxes de Computadors i Sistemes Distribuïts - Ponències/Comunicacions de congressos [199]
  • Departament d'Arquitectura de Computadors - Ponències/Comunicacions de congressos [1.874]
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