CrowdFL: A Marketplace for Crowdsourced Federated Learning
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.
Cite
Text
Feng et al. "CrowdFL: A Marketplace for Crowdsourced Federated Learning." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21715Markdown
[Feng et al. "CrowdFL: A Marketplace for Crowdsourced Federated Learning." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/feng2022aaai-crowdfl/) doi:10.1609/AAAI.V36I11.21715BibTeX
@inproceedings{feng2022aaai-crowdfl,
title = {{CrowdFL: A Marketplace for Crowdsourced Federated Learning}},
author = {Feng, Daifei and Helena, Cicilia and Lim, Wei Yang Bryan and Ng, Jer Shyuan and Jiang, Hongchao and Xiong, Zehui and Kang, Jiawen and Yu, Han and Niyato, Dusit and Miao, Chunyan},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2022},
pages = {13164-13166},
doi = {10.1609/AAAI.V36I11.21715},
url = {https://mlanthology.org/aaai/2022/feng2022aaai-crowdfl/}
}