Federated Learning on 5G Edge for Industrial Internet of Things
Xiaoli Liu, Xiang Su, Guillermo del Campo, Jacky Cao, Boyu Fan, Edgar Saavedra, Asunción Santamaría, Juha Röning, Pan Hui, Sasu Tarkoma
Abstract
Industry 4.0, leveraging the Internet of Things (IoT) and Artificial Intelligence (AI), is a key enabler for many automated processes in modernized industrial applications. This paper addresses significant challenges pertaining to sensing and data analytics by connecting a large number of industrial IoT (IIoT) devices and deploying federated learning on 5G edge networks. We envision a federated learning-based 5G edge architecture for IIoT and develop an AI algorithm, i.e., an LSTM autoencoder algorithm for anomaly detection, on the 5G edge. We conduct comprehensive scalability analytics of communication and computation resources on our 5G edge IoT testbed. Our experimentation verifies that 1) federated AI algorithms can be deployed on 5G edge servers for latency-sensitive analytics, and 2) 5G edge supports scalable deployment of IIoT devices with low latency.