Split learning en embebidos con TensorFlow lite
Esta tesis se presenta una red neuronal en split utilizando las librerias de tensorflowlite.
- Autores:
-
Tirado Gómez, Vilma Marcela
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2022
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/59355
- Acceso en línea:
- http://hdl.handle.net/1992/59355
- Palabra clave:
- Edge Computing
Split learning
Electronic embedded systems
TinyML.
Ingeniería
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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Split learning en embebidos con TensorFlow lite |
| title |
Split learning en embebidos con TensorFlow lite |
| spellingShingle |
Split learning en embebidos con TensorFlow lite Edge Computing Split learning Electronic embedded systems TinyML. Ingeniería |
| title_short |
Split learning en embebidos con TensorFlow lite |
| title_full |
Split learning en embebidos con TensorFlow lite |
| title_fullStr |
Split learning en embebidos con TensorFlow lite |
| title_full_unstemmed |
Split learning en embebidos con TensorFlow lite |
| title_sort |
Split learning en embebidos con TensorFlow lite |
| dc.creator.fl_str_mv |
Tirado Gómez, Vilma Marcela |
| dc.contributor.advisor.none.fl_str_mv |
García Cárdenas, Juan José Sierra Alarcón, Sebastián Segura Quijano, Fredy Enrique |
| dc.contributor.author.none.fl_str_mv |
Tirado Gómez, Vilma Marcela |
| dc.contributor.jury.none.fl_str_mv |
Giraldo Trujillo, Luis Felipe |
| dc.subject.keyword.none.fl_str_mv |
Edge Computing Split learning Electronic embedded systems TinyML. |
| topic |
Edge Computing Split learning Electronic embedded systems TinyML. Ingeniería |
| dc.subject.themes.es_CO.fl_str_mv |
Ingeniería |
| description |
Esta tesis se presenta una red neuronal en split utilizando las librerias de tensorflowlite. |
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2022 |
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2022-07-29T18:28:57Z |
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2022-07-29T18:28:57Z |
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2022-07-01 |
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Trabajo de grado - Pregrado |
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spa |
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spa |
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Mahadev Satyanarayanan. The emergence of edge computing. In: Computer 50.1 (2017), pp. 30-39. |
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Ziming Zhao et al. Edge computing: platforms, applications and challenges. In: J. Comput. Res. Dev 55.2 (2018), pp. 327-337. Xuehai Hong and Yang Wang. Edge computing technology: development and countermeasures. In: Strategic Study of Chinese Academy of Engineering 20.2 (2018), pp. 20-26. Keith Bonawitz et al. Towards Federated Learning at Scale: System Design. In: (2019). doi: 10 . 48550 / ARXIV . 1902 . 01046. url: https : // arxiv.org/abs/1902.01046. Dinh C. Nguyen et al. Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges. In: (2021). doi: 10.48550/ARXIV. 2104.01776. url: https://arxiv.org/abs/2104.01776. H. Brendan McMahan et al. Communication-Efficient Learning of Deep Networks from Decentralized Data. In: (2016). doi: 10 . 48550 / ARXIV. 1602.05629. url: https://arxiv.org/abs/1602.05629. |
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8 páginas |
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Universidad de los Andes |
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Ingeniería Electrónica |
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Facultad de Ingeniería |
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Departamento de Ingeniería Eléctrica y Electrónica |
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The emergence of edge computing. In: Computer 50.1 (2017), pp. 30-39.Ziming Zhao et al. Edge computing: platforms, applications and challenges. In: J. Comput. Res. Dev 55.2 (2018), pp. 327-337.Xuehai Hong and Yang Wang. Edge computing technology: development and countermeasures. In: Strategic Study of Chinese Academy of Engineering 20.2 (2018), pp. 20-26.Keith Bonawitz et al. Towards Federated Learning at Scale: System Design. In: (2019). doi: 10 . 48550 / ARXIV . 1902 . 01046. url: https : // arxiv.org/abs/1902.01046.Dinh C. Nguyen et al. Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges. In: (2021). doi: 10.48550/ARXIV. 2104.01776. url: https://arxiv.org/abs/2104.01776.H. Brendan McMahan et al. Communication-Efficient Learning of Deep Networks from Decentralized Data. 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