A Review of Privacy-preserving Federated Learning for the Internet-of-Things

Understand

The Internet-of-Things (IoT) generates vast quantities of data, much of it attributable to individuals' activity and behaviour.

• Gathering personal data and performing 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.
• However, 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 as an approach for performing machine learning on distributed data with the goal of protecting the privacy of user-generated data as well as reducing communication costs associated with data transfer.