Chong Shen Ng
Dr. Chong Shen Ng is a Research Engineer at Flower Labs with over a decade of experience in both research and industry, specializing in federated learning, data science, and parallel computing. As a key developer, he focuses on scaling Flower to deploy privacy-enhanced distributed AI solutions for real-world applications. Chong Shen is passionate about contributing to the open-source community, developing trustworthy AI systems through federated learning, and advancing edge AI technologies. A dedicated advocate for open-source software, he has co-chaired PyData Global events and volunteered at SciPy and PyData London conferences.
@chongshenng
Session
Since it’s introduction in 2016, Federated Learning (FL) has become a key paradigm to AI models in scenarios when training data cannot leave its source. This applies in many industrial settings where centralizing data is challenging due to a combination of reasons, including but not limited to privacy, legal, and logistics.
The main focus of this tutorial is to introduce an alternative approach to training AI models that is straightforward and accessible. We’ll walk you through the basics of an FL system, how to iterate on your workflow and code in a research setting, and finally deploy your code to a production environment. You will learn all of these approaches using a real-world application based on open-sourced datasets, and the open-source federated AI framework, Flower, which is written in Python and designed for Python users. Throughout the tutorial, you’ll have access to hands-on open-sourced code examples to follow along.