Skip to main content

Research Repository

Advanced Search

A review of privacy-preserving federated learning for the Internet-of-Things

Briggs, Christopher; Fan, Zhong; Andras, Peter


Christopher Briggs

Zhong Fan

Profile Image

Prof Peter Andras
Dean of School of Computing Engineering and the Built Environment


Muhammad Habib ur Rehman

Mohamed Medhat Gaber


The Internet-of-Things (IoT) generates vast quantities of data. Much of this data is attributable to human activities and behavior. Collecting personal data and executing machine learning tasks on this data in a central location presents a significant privacy risk to individuals as well as challenges with communicating this data to the cloud (e.g. where data is particularly large or updated very frequently). Analytics based on machine learning and in particular deep learning benefit greatly from large amounts of data to develop high-performance predictive models. This work reviews federated learning (FL) as an approach for performing machine learning on distributed data to protect the privacy of user-generated data. We highlight pertinent challenges in an IoT context such as reducing communication costs associated with data transmission, learning from data under heterogeneous conditions, and applying additional privacy protections to FL. Throughout this review, we identify the strengths and weaknesses of different methods applied to FL, and finally, we outline future directions for privacy-preserving FL research, particularly focusing on IoT applications.


Briggs, C., Fan, Z., & Andras, P. (2021). A review of privacy-preserving federated learning for the Internet-of-Things. In M. Habib ur Rehman, & M. Medhat Gaber (Eds.), Federated Learning Systems: Towards Next-Generation AI (21-50). Springer.

Online Publication Date Jun 12, 2021
Publication Date 2021
Deposit Date Nov 2, 2021
Publisher Springer
Pages 21-50
Series Title Studies in Computational Intelligence
Series Number 965
Book Title Federated Learning Systems: Towards Next-Generation AI
ISBN 978-3-030-70603-6
Public URL