Edge-Optimized Federated Learning Using Differential Privacy for Secure Patient Monitoring in AI-Driven MIoT Healthcare Systems

Abstract

Real-time surveillance, predictive diagnosis, informed judgement are changing healthcare of AI and
MIoT. Technology-blend models contribute to this development. Nevertheless, the IoT devices produce
high sensitive patient data that implicates privacy, security and scalability issues. To provide an AI
safeguards and effective patient monitoring mechanism in burgeoning MIoT AICT, we introduce edge-
centric federated learning with differential privacy for the purpose of realistic cost-effective safe patient
monitoring. Patient entries are saved to (and reside with at) the network edge in built-in bedside or IoT
devices.