Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156042
Title: CrowdFL: a marketplace for crowdsourced federated learning
Authors: Feng, Daifei
Helena, Cicilia
Lim, Bryan Wei Yang
Ng, Jer Shyuan
Jiang, Hongchao
Xiong, Zehui
Kang, Jiawen
Yu, Han
Niyato, Dusit
Miao, Chunyan
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Feng, D., Helena, C., Lim, B. W. Y., Ng, J. S., Jiang, H., Xiong, Z., Kang, J., Yu, H., Niyato, D. & Miao, C. (2022). CrowdFL: a marketplace for crowdsourced federated learning. Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22), 36(11), 13164-13166. https://dx.doi.org/10.1609/aaai.v36i11.21715
Project: AISG2-RP-2020-019 
NRF-NRFI05- 2019-0002 
Alibaba-NTU-AIR2019B1 
NTU-SDU-CFAIR (NSC- 2019-011) 
A20G8b0102 
Conference: Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22)
Abstract: 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.
URI: https://hdl.handle.net/10356/156042
URL: https://ojs.aaai.org/index.php/AAAI/article/view/21715
DOI: 10.1609/aaai.v36i11.21715
Schools: School of Computer Science and Engineering 
Research Centres: Alibaba-NTU Singapore Joint Research Institute
Rights: © 2022 Association for the Advancement of Artificial Intelligence. All rights reserved. This paper was published in Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22) and is made available with permission of Association for the Advancement of Artificial Intelligence.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCBE Conference Papers

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