Implementing and evaluating Google federated learning algorithms

Implementing and evaluating Google federated learning algorithms

Amid data privacy concerns, Federated Learning(FL) has emerged as a promising machine learning paradigm that enables privacy-preserving collaborative model training. However, there exists the need for a platform that matches data owners (supply) with model requesters (demand). This paper will dee...

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Main Author: Cicilia Helena
Other Authors: Dusit Niyato
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Engineering::Computer science and engineering
Online Access:https://hdl.handle.net/10356/148007
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1480072021-04-22T04:34:57Z Implementing and evaluating Google federated learning algorithms Cicilia Helena Dusit Niyato School of Computer Science and Engineering DNIYATO@ntu.edu.sg Engineering::Computer science and engineering Amid data privacy concerns, Federated Learning(FL) has emerged as a promising machine learning paradigm that enables privacy-preserving collaborative model training. However, there exists the need for a platform that matches data owners (supply) with model requesters (demand). This paper will deep dive into some of the components of a working prototype of CrowdFL, a platform for facilitating the crowdsourcing of FL models. It supports client selection, model training, and reputation management, which are essential for the FL crowdsourcing operations. By implementing model training on actual mobile devices, we demonstrate that the platform improves model performance and training efficiency. Bachelor of Engineering (Computer Engineering) 2021-04-22T04:34:03Z 2021-04-22T04:34:03Z 2021 Final Year Project (FYP) Cicilia Helena (2021). Implementing and evaluating Google federated learning algorithms. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148007 https://hdl.handle.net/10356/148007 en SCSE20 - 0077 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Cicilia Helena
Implementing and evaluating Google federated learning algorithms
description Amid data privacy concerns, Federated Learning(FL) has emerged as a promising machine learning paradigm that enables privacy-preserving collaborative model training. However, there exists the need for a platform that matches data owners (supply) with model requesters (demand). This paper will deep dive into some of the components of a working prototype of CrowdFL, a platform for facilitating the crowdsourcing of FL models. It supports client selection, model training, and reputation management, which are essential for the FL crowdsourcing operations. By implementing model training on actual mobile devices, we demonstrate that the platform improves model performance and training efficiency.
author2 Dusit Niyato
author_facet Dusit Niyato
Cicilia Helena
format Final Year Project
author Cicilia Helena
author_sort Cicilia Helena
title Implementing and evaluating Google federated learning algorithms
title_short Implementing and evaluating Google federated learning algorithms
title_full Implementing and evaluating Google federated learning algorithms
title_fullStr Implementing and evaluating Google federated learning algorithms
title_full_unstemmed Implementing and evaluating Google federated learning algorithms
title_sort implementing and evaluating google federated learning algorithms
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/148007
_version_ 1698713637619761152

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