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arXiv:2512.03358

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

Dev Gurung, Shiva Raj Pokhrel

Published Dec 3, 2025• 0 citations
arXivHTMLPDF

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.

DOI
10.48550/arxiv.2512.03358

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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 nnn 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.
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The preservation of privacy is a critical concern in the implementation of artificial intelligence on sensitive training data. There are several techniques to preserve data privacy but quantum computations are inherently more secure due to the nocloning theorem, resulting in a most desirable computational platform on top of the potential quantum advantages. There have been prior works in protecting data privacy by Quantum Federated Learning (QFL) and Quantum Differential Privacy (QDP) studied independently. However, to the best of our knowledge, no prior work has addressed both QFL and QDP together yet. Here, we propose to combine these privacy-preserving methods and implement them on the quantum platform, so that we can achieve comprehensive protection against data leakage (QFL) and model inversion attacks (QDP). This implementation promises more efficient and secure artificial intelligence. In this paper, we present a successful implementation of these privacy-preservation methods by performing the binary classification of the Cats vs Dogs dataset. Using our quantum-classical machine learning model, we obtained a test accuracy of over 0.98, while maintaining epsilon values less than 1.3. We show that federated differentially private training is a viable privacy preservation method for quantum machine learning on Noisy Intermediate-Scale Quantum (NISQ) devices.
In recent years, the growing demand for secure, efficient, and scalable machine learning has highlighted significant challenges in traditional Federated Learning (FL) systems. These challenges include computational inefficiencies in handling large-scale, high-dimensional data, privacy concerns, and the limitations of classical hardware in solving complex optimization problems. Additionally, the increasing complexity of data and the need for more robust privacy-preserving methods have pushed the boundaries of classical FL. To address these issues, this paper presents a detailed framework for Quantum Federated Learning (QFL), where quantum computing and FL converge to enable distributed quantum model training with enhanced privacy preservation. The system is modeled using local quantum models on clients and a global aggregation strategy at the server. We develop quantum gradient-based optimization with quantum cross-entropy loss, privacy preservation through encryption, and robustness to quantum noise. A set of theorems is provided to prove the correctness and efficiency of the model, supported by rigorous mathematical formulations. We used IBM Qiskit for the simulation and compared our proposed QFL with classical FL. The results demonstrate that our QFL outperformed classical FL in terms of accuracy, precision, recall, and F1 score.
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The mix of quantum computing and machine learning has introduced strong quantum models able to resolve challenging tasks efficiently. Still, the use of centralized QML systems could present challenges with privacy because sharing quantum data with untrusted aggregators is especially risky. This study describes a method called Privacy-Preserving Quantum Federated Learning (PQFL), which allows multiple clients to jointly train quantum models while maintaining data confidentiality. This system offers security by combining QHE and QDP to cover the processes of aggregating protected models and enforcing privacy on the data. Hybrid quantum-classical studies reveal that PQFL is both accurate and advanced in protecting privacy and communication security. According to these results, PQFL works effectively in privacy-friendly domains including healthcare, genomics, and finance, where data needs to be managed securely.
In this study, we explore the innovative domain of Quantum Federated Learning (QFL) as a framework for training Quantum Machine Learning (QML) models via distributed networks. Conventional machine learning models frequently grapple with issues about data privacy and the exposure of sensitive information. Our proposed Federated Quantum Neural Network (FedQNN) framework emerges as a cutting-edge solution, integrating the singular characteristics of QML with the principles of classical federated learning. This work thoroughly investigates QFL, underscoring its capability to secure data handling in a distributed environment and facilitate cooperative learning without direct data sharing. Our research corroborates the concept through experiments across varied datasets, including genomics and healthcare, thereby validating the versatility and efficacy of our FedQNN framework. The results consistently exceed 86% accuracy across three distinct datasets, proving its suitability for conducting various QML tasks. Our research not only identifies the limitations of classical paradigms but also presents a novel framework to propel the field of QML into a new era of secure and collaborative innovation.