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http://hdl.handle.net/10609/126546
Title: | Federated learning network: Training distributed machine learning models with the federated learning paradigm |
Author: | Yáñez Parareda, Eduardo |
Tutor: | Freitag, Felix |
Abstract: | This project has two main goals. On one hand, the study of the concept of Federated Learning, which was coined 3 years ago by a team of engineers from Google. On the other hand, it is intended to develop a software which serves as a tool to create a distributed network of devices, capable of applying Federated Learning with different Machine Learning models. The result is a functional software that meets our initial purpose and can apply Federated Learning in a distributed environment, allowing us to validate in a practical way, the initial concepts of study. Throughout this project, the most important concepts of Federated Learning are presented, as well as some of the software frameworks that are starting to emerge from it. |
Keywords: | federated learning machine learning distributed computation privacy |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | Jan-2021 |
Appears in Collections: | Trabajos finales de carrera, trabajos de investigación, etc. |
Files in This Item:
File | Description | Size | Format | |
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Federated_Learning_Network-master-project-report_EN.pdf | Master project's report | 5,95 MB | Adobe PDF | View/Open |
Federated_Learning_Network-presentacion_ES.mp4 | Project's presentation in Spanish | 108,66 MB | MP4 | View/Open |
federated-learning-network-main.zip | Main source code | 621,3 kB | Text | View/Open |
federated-learning-simulation-main.zip | Source code of the small simulator | 6,11 kB | Text | View/Open |
eypTFM0121memory.pdf | 5,93 MB | Adobe PDF | View/Open |
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