CrowdFL: a marketplace for crowdsourced federated learning

• 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
• Institution: Nanyang Technological University

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.