Federated Learning for Privacy-Preserving Smart Healthcare: An Architectural Overview

Abstract

The combination of Artificial Intelligence (AI) and Internet of Things (IoT) in the healthcare realm has led to intelligent healthcare systems that provide real-time and data-driven medical services that are personalised to an individual. Yet, due to the favorable nature of healthcare data, privacy, security, and regulatory compliance (such as HIPAA and GDPR) become challenging. Existing, traditional centralized machine learning paradigms are often a poor fit for healthcare because they require the consolidation of all the data, which can heavily erode patient privacy. As a new approach, Federated Learning (FL) allows model training over various decentralized devices without patients’ data being sent to a central server. This provides an architectural overview of FL in privacy-preserving smart healthcare. The study then investigates the fundamental components, enabling technologies, communication protocols, security enhancements and performance measurement metrics for FL architectures. We also examine real-world use cases, including remote patient monitoring (RPM), disease prediction and medical image analysis. Moreover, an extensive literature study, comparative analysis and a proposed framework which integrates differential privacy and secure multiparty computation are presented to increase the security of the data as well as model robustness. Latency, model accuracy and communication efficiency are discussed using simulated datasets and potential key performance indicators of a hybrid system where a group of real-world participants led by an expert proxy provide input to an ML engine. This culminates in a detailed discussion on the challenges, future directions and the transformational potential of federated learning in establishing a truly secure and intelligent smart healthcare ecosystem.