A marketplace for crowdsourced federated learning

A marketplace for crowdsourced federated learning

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 presents C...

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Bibliographic Details
Main Author: Feng, Daifei
Other Authors: Dusit Niyato
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Online Access:https://hdl.handle.net/10356/148179
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Institution: Nanyang Technological University
Language: English
Description
Summary: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 presents CrowdFL, a marketplace for facilitating the crowdsourcing of FL model training. By implementing model training on actual mobile devices, we demonstrate that the platform improves model performance and training efficiency. To the best of our knowledge, it is the first platform to support crowdsourcing-based federated learning on edge devices.

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