Comparación de técnicas distribuidas de aprendizaje automático aplicadas a datos médicos disponibles en abierto
Distributed machine/deep learning refers to algorithms and systems designed to enhance performance, preserve privacy, and scale to larger training data and models. The aim of this study is to compare the performance of different distributed machine learning techniques, such as federated learning, go...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad de Cantabria (UC) |
| Repositorio: | UCrea Repositorio Abierto de la Universidad de Cantabria |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unican.es:10902/35359 |
| Acceso en línea: | https://hdl.handle.net/10902/35359 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Distributed learning Federated learning Medical data Privacy Aprendizaje automático Aprendizaje distribuido Aprendizaje federado Datos médicos Privacidad |
| Sumario: | Distributed machine/deep learning refers to algorithms and systems designed to enhance performance, preserve privacy, and scale to larger training data and models. The aim of this study is to compare the performance of different distributed machine learning techniques, such as federated learning, gossip learning, or ring all-reduce architecture. To achieve this, their application is proposed using artificial neural networks on an openly available medical dataset. Various metrics will be evaluated based on the architecture configuration and the number of rounds carried out. The implementation of the three architectures using Python is proposed in a scenario where data distribution is simulated. All implemented code can be openly accessed. |
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