Scaling Trust in Quantum Federated Learning: A Multi-Protocol Privacy Design
By: Dev Gurung, Shiva Raj Pokhrel
Potential Business Impact:
Keeps private data safe during AI training.
Quantum Federated Learning (QFL) promises to revolutionize distributed machine learning by combining the computational power of quantum devices with collaborative model training. Yet, privacy of both data and models remains a critical challenge. In this work, we propose a privacy-preserving QFL framework where a network of $n$ quantum devices trains local models and transmits them to a central server under a multi-layered privacy protocol. Our design leverages Singular Value Decomposition (SVD), Quantum Key Distribution (QKD), and Analytic Quantum Gradient Descent (AQGD) to secure data preparation, model sharing, and training stages. Through theoretical analysis and experiments on contemporary quantum platforms and datasets, we demonstrate that the framework robustly safeguards data and model confidentiality while maintaining training efficiency.
Similar Papers
Quantum Vanguard: Server Optimized Privacy Fortified Federated Intelligence for Future Vehicles
Cryptography and Security
Makes self-driving cars safer from hackers.
When Quantum Federated Learning Meets Blockchain in 6G Networks
Cryptography and Security
Makes smart devices learn together safely and fast.
Enhancing Gradient Variance and Differential Privacy in Quantum Federated Learning
Quantum Physics
Makes AI learn better and safer with less noise.