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

Briggs, Christopher; Fan, Zhong; Andras, Peter

Abstract

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.

• 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
• DOI: https://doi.org/10.1007/978-3-030-70604-3_2
• Public URL: http://researchrepository.napier.ac.uk/Output/2808669