Scaling Trust in Quantum Federated Learning: A Multi-Protocol Privacy Design

TL;DR

This paper introduces a multi-protocol privacy framework for Quantum Federated Learning that combines quantum cryptography and advanced quantum algorithms to enhance data and model security without sacrificing training performance.

Contribution

It presents a novel multi-layered privacy-preserving QFL framework integrating SVD, QKD, and AQGD, addressing key security challenges in quantum distributed learning.

Findings

Framework effectively protects data and model confidentiality.

Maintains training efficiency on quantum platforms.

Theoretical and experimental validation supports robustness.

Abstract

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.