A Platform for Federated Learning on the Edge: a Video Analysis Use Case

Abstract

Recently, both scientific and industrial communities have highlighted the importance to run Machine Learning (ML) applications on Edge computing closer to the end-user and to managed raw data, for many reasons including quality of service (QoS) and security. However, due to the limited computing, storage and network resources at the Edge, several ML algorithms have been re-designed to be deployed on Edge devices. In this paper, we want to explore in detail Edge Federation for supporting ML-based solutions. In particular, we present a new platform for the deployment and the management of complex services at the Edge. It provides an interface that allows us to arrange applications as a collection of interconnected lightweight loosely-coupled services (i.e., microservices) and enables their management across Federated Edge devices through the abstraction of the underlying clusters of physical devices. The proposed solution is validated by a use case related to video analysis in the morphological field.