A Framework for Edge-Assisted Healthcare Data Analytics using Federated Learning

Abstract

With the emergence of wearable technology, IoT, and Edge computing, the nature of healthcare is rapidly shifting towards digital health aided by these ICT technologies. At the same time, consumer devices, such as smart, wearable fitness watches are gaining market share as a way to monitor physical activity and wellness. Despite these advances, and their ability to capture longitudinal behavioural patterns, these devices have yet to be fully leveraged within the healthcare system. If the user-generated data from such devices could be collected without com-promising an individual’s privacy, these insights could comprise part of a more holistic and preventative healthcare solution. In this article, we propose an Edge-assisted data analytics frame-work that uses Federated Learning to re-train local machine learning models using user-generated data. This framework could leverage pre-trained models to extract user-customized insights while preserving privacy and Cloud resources. We also identify some potential application scenarios and discuss research challenges to be explored within the proposed framework.