QFed: A Secure Federated Learning Framework for IoMT Using Quantum Cryptography
Paper Title: QFed: A Secure Federated Learning Framework for IoMT Using Quantum Cryptography
Journal Name: IEEE Journal of Biomedical and Health Informatics
Abstract: In the increasingly interconnected domain of the Internet of Medical Things (IoMT), ensuring the security and privacy of patient medical data is critical. Federated Learning (FL) offers a promising approach for collaborative training of security models across various medical devices to enable autonomous attack detection while preserving sensitive data. However, FL faces significant security and privacy challenges, including adversarial interception during communication between clients and the FL server, and malicious interference from an honest-but-curious FL aggregator. This paper addresses these issues by introducing QFed, a quantum-federated framework designed to provide enhanced security and privacy in IoMT environments.