CrowdFL: a marketplace for crowdsourced federated learning

CrowdFL: 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 a need for a platform that matches data owners (supply) with model requesters (demand). In this paper, we prese...

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Bibliographic Details
Main Authors: Feng, Daifei, Helena, Cicilia, Lim, Bryan Wei Yang, Ng, Jer Shyuan, Jiang, Hongchao, Xiong, Zehui, Kang, Jiawen, Yu, Han, Niyato, Dusit, Miao, Chunyan
Other Authors: School of Computer Science and Engineering
Format: Conference or Workshop Item
Language:English
Published: 2022
Subjects:
Engineering::Computer science and engineering
Federated Learning
Crowdsourcing
Online Access:https://hdl.handle.net/10356/156042
https://ojs.aaai.org/index.php/AAAI/article/view/21715
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Institution: Nanyang Technological University
Language: English
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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 a need for a platform that matches data owners (supply) with model requesters (demand). In this paper, we present CrowdFL, a platform to facilitate the crowdsourcing of FL model training. It coordinates client selection, model training, and reputation management, which are essential steps for the FL crowdsourcing operations. 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 FL on edge devices.

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