Enhancing Differential Privacy in Federated Learning via Quantum Computation and Algorithms

The exponential expansion of data-driven technologies underscores the critical need for robust privacy safeguards. This study introduces an innovative privacy-preserving framework that combines Differential Privacy (DP) and Federated Learning (FL) with the cutting-edge capability of quantum computing. Leveraging Quantum Random Number Generators (QRNGs), the framework incorporates truly random quantum-generated noise into the differential privacy mechanism, substantially enhancing the security of gradient updates within machine learning models. This noise is specifically added to the gradient updates during the optimization process, using various optimizers. Quantum mechanics' inherent randomness ensures unpredictability, fortifying defenses against sophisticated attacks. This research signifies a significant step towards the development of secure and ethical AI technologies. By integrating quantum computing's random number generation capabilities into privacy-preserving mechanisms, the research paves the way for enhanced data security across various sectors. The findings indicate a promising path for further investigation and experimentation, with potential applications in fields demanding high-stakes data protection. This fusion of quantum computing and privacy-preserving techniques not only enhances the confidentiality of sensitive information but also sets a new standard for privacy in the age of data-driven innovation.