AAAI Conference 2022 System Paper
CrowdFL: A Marketplace for Crowdsourced Federated Learning
- Daifei Feng
- Cicilia Helena
- Wei Yang Bryan Lim
- Jer Shyuan Ng
- Hongchao Jiang
- Zehui Xiong
- Jiawen Kang
- Han Yu
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.