Edge Intelligence: Federated Learning-Based Privacy Protection Framework for Smart Healthcare Systems

Federated learning methods offer secured monitor services and privacy-preserving paradigms to end-users and organisations in the Internet of Things networks such as smart healthcare systems. Federated learning has been coined to safeguard sensitive data, and its global aggregation is often based on a centralised server. This design is vulnerable to malicious attacks and could be breached by privacy attacks such as inference and free-riding, leading to inefficient training models. Besides, uploaded analysing parameters by patients can reveal private information and the threat of direct manipulation by the central server. To address these issues, we present a three-fold Federated Edge Aggregator, the so-called Edge Intelligence, a federated learning-based privacy protection framework for safeguarding Smart Healthcare Systems at the edge against such privacy attacks. We employ an iteration-based Conventional Neural Network (CNN) model and artificial noise functions to balance privacy protection and model performance. A theoretical convergence bound of Edge Intelligence on the trained federated learning model's loss function is also introduced here. We evaluate and compare the proposed framework with the recently established methods using model performance and privacy budget on popular and recent datasets: MNIST, CIFAR10, STL10, and COVID19 chest x-ray. Finally, the proposed framework achieves 90% accuracy and a high privacy rate demonstrating better performance than the baseline technique.

I. Introduction

The Internet of Things (IoT) growth enables smart healthcare systems to step forward for inaccessible patients during critical situations like pandemics and ambient assisted living (AAL). In this situation, a Smart Health Systems (SHS) [1] offers smart applications for real-time health monitoring, early-stage detection, and cognitive decision-making by gathering patients' immediate physiological records at any time and from any location via an IoT network. As a result, the demand for IoT systems is rapidly increasing, with an estimated value of $158 billion by 2022. Moreover, the recent COVID-19 outbreak has accelerated this industry's hyper-growth [2]. Sensitive information such as valuable health insights data collected through high technology devices in IoT systems needs to choose an efficient smart sharing platform that meets the high-level privacy of Australian Privacy law with HIPAA [3], [4] compliant.