A Practical Federated Learning Framework for Small Number of Stakeholders
Christian Schneebeli, Saikishore Kalloori, Severin Klingler · 2021
Federated Learning (FL) allows to collaboratively build machine learning models between different entities without the need for sharing or gathering the data. In FL, typically there is a global server and a set of clients (stakeholders) to build shared machine learning models. In contrast to distributed machine learning, the controller of the training process (here the global server) never sees the data of the stakeholders participating in FL. Every stakeholder owns his own data and doesn't share it. During the training and learning process, only the model updates (e.g. gradients) are shared. To our best of knowledge, we did not find a publicly available practical federated learning framework for stakeholders. We have built a framework that enables FL for a small number of stakeholders. In the paper, we describe the framework architecture, communication protocol, and algorithms. Our framework is open-sourced and it is easy to set up for stakeholders and ensures that no private information is leaked during the training process.