On‐Demand Deployment of Edge Cloud Infrastructures for Federated Learning

Abstract

ABSTRACT Background Federated learning on the edge allows the use of more computing capacity and more complex training models while providing higher accuracy and bandwidth savings. However, the deployment and management of edge cloud infrastructures is still challenging due to its highly distributed nature. Aims This paper presents the deployment of a real framework for federated learning on top of a real geo‐distributed edge cloud infrastructure. Methods The OpenNebula cloud platform is used to deploy an edge cloud infrastructure and a federated learning framework on top of it, as well as to manage and orchestrate the infrastructure. Results A global edge‐based infrastructure was deployed in 16 min for just $6 per hour, reducing execution time and increasing the accuracy of the trained model. Conclusion Results show the feasibility, performance, and cost efficiency of the solution, which provides an easy, fast, and inexpensive way to perform edge‐based federated learning.