Towards Developing a Global Federated Learning Platform for IoT

Abstract

Federated learning (FL) is an approach that enables collaborative machine learning (ML) without sharing data over the network. Internet of Things (IoT) and Industry 4.0 are promising areas for FL adoption. Nevertheless, there are several challenges to overcome before the deployment of FL methods in existing large-scale IoT environments. In this paper, we present one step further toward the adoption of FL systems for IoT. More specifically, we developed a prototype that enables distributed ML model deployment, federated task orchestration, and monitoring of system state and model performance. We tested the prototype on a network that contains multiple Raspberry Pi for a use case of modeling the states of conveyors in an airport.