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 andMIoT. Technology-blend models contribute to this development. Nevertheless, the IoT devices producehigh sensitive patient data that implicates privacy, security and scalability issues. To provide an AIsafeguards 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 patientmonitoring. Patient entries are saved to (and reside with at) the network edge in built-in bedside or IoTdevices.
Fields of Study & Subjects
Privacy-Preserving Technologies in Data
IoT and Edge/Fog Computing
Internet of Things and AI
Publication details
- Journal / source
- The Bioscan.
- Volume
- 21
- Issue
- 1
- Pages
- 1315–1336
- Publication date
- 11 January 2026
- Language
- EN