On the Physical Layer Security of Federated Learning Based IoMT Networks

Area of Science:

• Computer Science
• Cybersecurity
• Telecommunications

Background:

• The Internet of Medical Things (IoMT) integrates diverse medical devices and data platforms via wireless communication.
• Federated Learning (FL) offers collaborative machine learning with reduced communication overhead and improved security, making it suitable for IoMT.
• Privacy preservation is a critical concern in IoMT due to sensitive health data.

Purpose of the Study:

• To provide an overview of Federated Learning architectures and their application in IoMT.
• To explore the role of Physical Layer Security (PLS) in enhancing data privacy for FL-based IoMT.
• To identify research challenges and future opportunities in PLS-assisted FL for IoMT.

Main Methods:

• Review of existing Federated Learning architectures and their suitability for IoMT environments.
• Analysis of Physical Layer Security (PLS) techniques for privacy preservation in FL-based IoMT.
• A case study simulating clustered IoMT devices to evaluate network secrecy rates.

Main Results:

• Clustering IoMT devices, with a designated cluster head, significantly enhances the secrecy rate of FL-based IoMT networks compared to non-clustered approaches.
• PLS offers an efficient method for privacy preservation in FL-based IoMT systems.
• Identified key research challenges and future directions for PLS-assisted FL in IoMT.

Conclusions:

• Federated Learning presents a promising approach for secure and efficient data processing in IoMT.
• Physical Layer Security, particularly when combined with device clustering, is crucial for robust privacy in FL-based IoMT.
• Further research is needed to address the complexities and optimize PLS-assisted FL for real-world IoMT applications.