Federated Learning on 5G Edge for Industrial Internet of Things

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.

Authors

Institutions

Publication Details

Journal
IEEE Network
Published
2024-10-03
DOI
https://doi.org/10.1109/mnet.2024.3469988
Citations
11
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
3.82

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Federated Learning on 5G Edge for Industrial Internet of Things

Pan Hui, Jacky Cao, Guillermo del Campo, Xiang Su et al.
11 citations
IEEE Network
Privacy-Preserving Technologies in Data
3.82
article

Federated Learning on 5G Edge for Industrial Internet of Things

Pan Hui, Jacky Cao, Guillermo del Campo, Xiang Su, Sasu Tarkoma, Juha Röning, Asunción Santamaría, Edgar Saavedra, Xiaoli Liu, Boyu Fan
article en
11 citations

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.

IEEE NetworkVol. 39(1)
University of Helsinki (FI), Norwegian University of Science and Technology (NO), Universidad Politécnica de Madrid (ES), University of Oulu (FI)
Academy of Finland, NordForsk
Industry, innovation and infrastructure
Openalex Percentile: Top 6%
Privacy-Preserving Technologies in Data
3.82
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Federated Learning on 5G Edge for Industrial Internet of Things — Pan Hui, Jacky Cao, et al. · IEEE Network (2024) | TGRS Research Map | TGRS