Privacy-Preserving and Quantum-Resistant Federated Learning Framework for Fog Healthcare Systems

Abstract

Quantum computing is on the horizon and will destroy existing cryptography standards, putting digital healthcare system security and patient privacy at danger. For very private and secure communication in fog healthcare settings, this article presents a new Quantum-Resistant Federated Deep Learning (QR-FDL) Framework. The QR-FDL architecture incorporates a lattice-based technique (like Kyber) into the model aggregation stage of Federated Learning (FL), which is a kind of Post-Quantum Cryptography (PQC). We provide a solution that uses Differential Privacy (DP) to prevent attacks based on gradients in data reconstruction and guarantees quantum-era security for weights of models with gradients sent between the central server and decentralized fog nodes. The practical use of QR-FDL is shown by its rapid convergence rate and excellent classification accuracy of 91.5% in an empirical assessment including a medical imaging job, such as tumor classification. Critically, we measure the cryptographic overhead and demonstrate that, even if PQC causes a regulated latency rise the total FL round time is still tolerable for real-time fog settings. In order to implement quantum-secure along with privacy-preserving Deep Learning in mission-critical healthcare communications, this study presents the first proven, end-to-end solution. In addition to showing that federated medical facilities can be securely encrypted from end to end, this architecture uses differential privacy during model training to protect patients’ personal information. Through the research, we demonstrate its practical implementation in the near future for it is hard to balance the precision of modeled obstructions with cryptographic overhead that still remains acceptable. The results of this research lay the groundwork for medical AI systems of the future that are both secure and respectful of patients’ privacy.