PhD in Computer Science

Project Introduction: The rapid adoption of Internet of Medical Things (IoMT) and telemedicine has transformed healthcare delivery, enabling continuous patient monitoring and remote diagnostics. However, these systems face two critical challenges: (1) the looming threat of quantum computing, which can break classical cryptographic schemes, and (2) the need for privacy-preserving AI models that do not centralize sensitive patient data. Federated Learning (FL) offers a promising solution by enabling collaborative model training without raw data sharing, but it remains vulnerable to adversarial attacks and lacks quantum resilience. This project aims to address these gaps by integrating post-quantum cryptography (PQC) and privacy-preserving mechanisms into federated learning frameworks for secure, scalable, and trustworthy remote healthcare systems.