Please use this identifier to cite or link to this item: 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.

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Federated_Learning_Network-master-project-report_EN.pdfMaster project's report5,95 MBAdobe PDFThumbnail
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Federated_Learning_Network-presentacion_ES.mp4

Project's presentation in Spanish108,66 MBMP4View/Open
federated-learning-network-main.zipMain source code621,3 kBTextView/Open
federated-learning-simulation-main.zipSource code of the small simulator6,11 kBTextView/Open
eypTFM0121memory.pdf5,93 MBAdobe PDFThumbnail
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