CrowdFL: A Marketplace for Crowdsourced Federated Learning

• DOI: 10.1609/aaai.v36i11.21715
• Publication Date: 2022-07-04T10:10:11Z
• Authors: Daifei Feng; Cicilia Helena; Wei Yang Bryan Lim; Jer Shyuan Ng; Hongchao Jiang; Zehui Xiong; Jiawen Kang; Han Yu; Dusit Niyato; Chunyan Miao
• Keywords: Crowdsourcing; 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 need for platform matches owners (supply) with requesters (demand). In this paper, we present CrowdFL, to facilitate the crowdsourcing of FL It coordinates client selection, training, and reputation management, which are essential steps operations. By implementing training on actual mobile devices, demonstrate improves performance efficiency. To best our knowledge, it is first support crowdsourcing-based edge devices.