Implementing and evaluating Google federated learning algorithms

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 the need for a platform that matches data owners (supply) with model requesters (demand). This paper will deep dive into some of the components of a working prototype of CrowdFL, a platform for facilitating the crowdsourcing of FL models. It supports client selection, model training, and reputation management, which are essential for the FL crowdsourcing operations. By implementing model training on actual mobile devices, we demonstrate that the platform improves model performance and training efficiency.