Medical institutions face significant challenges in collaborative machine learning due to diverse feature distributions, strict privacy regulations, and the critical issue of data scarcity. Traditional federated learning approaches often struggle with poor generalization and inadequate privacy protection in these sensitive healthcare settings. This paper presents a Quantum-Enhanced Vertical Federated Learning (Q-VFL) framework that effectively combines quantum feature encoding with supervised contrastive learning. Our approach employs a three-stage quantum circuit architecture for robust feature encoding, followed by classical neural networks, implemented through a privacy-preserving split learning procedure. Experimental results across diverse medical imaging datasets demonstrate that the proposed Q-VFL framework achieves strong performance, with accuracy gains of up to 25 percentage points over quantum cross-entropy baselines and competitive results against classical methods. Beyond raw performance, the framework exhibits high clinical reliability and stability. Crucially, it provides stronger privacy guarantees: detailed analysis shows that Q-VFL reduces membership inference attack success rates by 10–15% relative to deep classical VFL baselines, with avg. 0.59 vs. 0.68. Q-VFL also achieves near-zero model inversion success on MedMNIST datasets (0.001). These findings identify Q-VFL as a promising solution for secure and collaborative medical AI.
Medical institutions face significant challenges in collaborative machine learning due to diverse feature distributions, strict privacy regulations, and the critical issue of data scarcity. Traditional federated learning approaches often struggle with poor generalization and inadequate privacy protection in these sensitive healthcare settings. This paper presents a Quantum-Enhanced Vertical Federated Learning (Q-VFL) framework that effectively combines quantum feature encoding with supervised contrastive learning. Our approach employs a three-stage quantum circuit architecture for robust feature encoding, followed by classical neural networks, implemented through a privacy-preserving split learning procedure. Experimental results across diverse medical imaging datasets demonstrate that the proposed Q-VFL framework achieves strong performance, with accuracy gains of up to 25 percentage points over quantum cross-entropy baselines and competitive results against classical methods. Beyond raw performance, the framework exhibits high clinical reliability and stability. Crucially, it provides stronger privacy guarantees: detailed analysis shows that Q-VFL reduces membership inference attack success rates by 10–15% relative to deep classical VFL baselines, with avg. 0.59 vs. 0.68. Q-VFL also achieves near-zero model inversion success on MedMNIST datasets (0.001). These findings identify Q-VFL as a promising solution for secure and collaborative medical AI.
Quantum federated learning (QFL) has recently emerged as a promising paradigm for privacy-preserving collaborative learning, yet most existing studies focus on horizontal federated learning and ignore the vertical federated learning (VFL), where parties hold complementary features of aligned samples. In this work, we propose Evidential Quantum Vertical Federated Learning (eviQVFL), a VFL-tailored QFL framework that employs a hybrid classical-quantum architecture for party-side feature processing, mapping local features into a quantum state. To preserve privacy and avoid information loss, party-side output states are directly transmitted to the server via quantum teleportation, and the server fuses the received quantum states with a non-parametric evidential fusion circuit grounded in evidence theory, followed by measurement-based inference. Extensive simulations on image classification and other real-world datasets demonstrate that eviQVFL consistently achieves higher classification accuracy than other classical and quantum baselines under comparable parameter budgets. Both empirical observations and theoretical analysis indicate that eviQVFL achieve less approximation error with limited quantum resources, while maintaining training stability and offering stronger feature privacy.
Healthcare organizations have a high volume of sensitive data and traditional technologies have limited storage capacity and computational resources. The prospect of sharing healthcare data for machine learning is more arduous due to government regulations related to patient privacy. In recent years, federated learning has offered a solution to accelerate distributed machine learning addressing concerns related to data privacy and governance. Currently, the blend of quantum computing and machine learning has experienced significant attention from academic institutions and research communities. The ultimate objective of this work is to develop a federated quantum machine learning framework (FQML) to tackle the optimization, security, and privacy challenges in the healthcare industry for medical imaging tasks. In this work, we propose a new methodology based on federated quantum convolutional neural networks (QCNNs) with distributed training across edge devices. To demonstrate the feasibility of the proposed FQML framework, we performed extensive experiments on two benchmark medical datasets (Pneumonia MNIST, and CT kidney disease analysis), which are non-independently and non-identically partitioned among the healthcare institutions/clients. The proposed framework is validated and assessed via large-scale simulations. Based on our results, the quantum simulation experiments achieve performance levels on par with well-known classical CNN models, 86.3% accuracy on the pneumonia dataset and 92.8% on the CT-kidney dataset, while requiring fewer model parameters and consuming less data. Moreover, a client selection mechanism is proposed to reduce the computation overhead at each communication round, which effectively improves the convergence rate.
Abstract This study presents a Federated Quantum Variational Quantum Classifier (FedVQC) framework designed to address critical challenges in healthcare data privacy and computational efficiency, particularly in the domain of genomic analysis. Conventional machine learning approaches in healthcare often face obstacles stemming from privacy concerns, regulatory constraints, and the fragmented nature of medical data across different institutions. The proposed FedVQC framework leverages the principles of federated learning and quantum machine learning to enable secure, decentralized training of predictive models without requiring the sharing of sensitive patient data. Extensive experimentation on multiple genomic datasets reveals a strong correlation between the number of participating institutions and the model's accuracy, with performance consistently improving as more clients contribute to the training process. The framework demonstrates high accuracy levels, ranging from 70% to 96%, across diverse datasets, highlighting its robustness and applicability in healthcare analytics. By harnessing the computational power of quantum computing and the collaborative nature of federated learning, the FedVQC framework provides a scalable and secure solution for genomic research. This study underscores the potential of quantum federated learning in advancing privacy-preserving precision medicine, large-scale genomic collaboration, and broader applications in bioinformatics, drug discovery, and personalized healthcare analytics
Healthcare industries frequently handle sensitive and proprietary data, and due to strict privacy regulations, they are often reluctant to share data directly. In today's context, Federated Learning (FL) stands out as a crucial remedy, facilitating the rapid advancement of distributed machine learning while effectively managing critical concerns regarding data privacy and governance. The fusion of federated learning and quantum computing represents a groundbreaking interdisciplinary approach with immense potential to revolutionize various industries, from healthcare to finance. In this work, we proposed a federated learning framework based on quantum tensor networks, which leverages the principles of many-body quantum physics. Currently, there are no known classical tensor networks implemented in federated settings. Furthermore, we investigated the effectiveness and feasibility of the proposed framework by conducting a differential privacy analysis to ensure the security of sensitive data across healthcare institutions. Experiments on popular medical image datasets show that the federated quantum tensor network model achieved a mean receiver-operator characteristic area under the curve (ROC-AUC) between 0.91-0.98. Experimental results demonstrate that the quantum federated global model, consisting of highly entangled tensor network structures, showed better generalization and robustness and achieved higher testing accuracy, surpassing the performance of locally trained clients under unbalanced data distributions among healthcare institutions.
AI-driven medical diagnostics increasingly requires collaborative model training across institutions, yet centralizing patient data conflicts with privacy regulations. Federated Learning enables distributed training without raw data sharing, but remains vulnerable to gradient inversion and model leakage attacks. Furthermore, harvest-now-decrypt-later attacks render computationally secure protocols insufficient for protecting long-lived medical records. Quantum communication offers information-theoretic security immune to such threats, making Quantum Federated Learning (QFL) a compelling framework for healthcare. However, practical deployment is constrained by communication overhead and quantum channel noise. We present a systematic quantitative study of communication, convergence, and noise trade-offs in QFL, introducing two complementary strategies to reduce quantum transmissions: (1) structured parameter reduction via light-cone feature selection in parameterized quantum circuits, and (2) a Hybrid QFL architecture that dynamically switches between centralized and decentralized aggregation. We show that Hybrid QFL reduces total quantum transmissions from 3TNMP, the cost of pure Centralized QFL, to {3t+2(T−t)}NMP over T rounds while preserving near-centralized convergence. We further demonstrate that decentralized aggregation is more noise-resilient under depolarizing noise, and evaluate Steane code-based quantum error correction in high-noise regimes. Our results provide an integrated design framework for communication-efficient, noise-aware QFL, clarifying practical trade-offs for scalable quantum-secure distributed learning in healthcare.
Abstract Healthcare organizations have a high volume of sensitive data and traditional technologies have limited storage capacity and computational resources. The prospect of sharing healthcare data for machine learning is more arduous due to firm regulations related to patient privacy. The balanced protection of confidentiality, integrity, and availability of healthcare data, has become a major concern beyond classical data security considerations. In recent years, federated learning offers a solution to accelerate distributed machine learning addressing concerns related to data privacy and governance. Currently, the blend of quantum computing and machine learning has experienced significant attention from academic institutions and research communities. Quantum computers have shown the potential to bring huge benefits to the healthcare sector through efficient distributed training across several quantum nodes. The ultimate objective of this work is to develop a quantum federated learning framework (QFL) to tackle the optimization, security, and privacy challenges in the healthcare and clinical industries for medical imaging tasks. In this work, we proposed federated quantum convolutional neural networks (QCNNs) with distributed training across edge devices. To demonstrate the feasibility of the proposed QFL framework, we performed extensive experiments on medical datasets (Pneumonia MNIST, and CT-kidney disease analysis), which are non-independently and non-identically partitioned among the healthcare institutions/clients. The quantum federated global model maintained a high classification testing accuracy and generalizability and outperformed the locally train clients regardless of how unbalanced the medical data is distributed among the clients. The global model achieved the best performance as compared to local clients in terms of the area under curve of the receiver operating characteristic curve (AUCROC) (0.953) and an average of (0.98) on all classes for predicting outcomes on pneumonia and CT-kidney datasets, respectively. Moreover, the client selection mechanism is proposed to reduce the computation overhead at each communication round, which effectively improves the convergence rate. The proposed quantum federated learning framework is validated and assessed via large-scale simulations. Based on our results from numerical simulations, the deployment of distributed and secure quantum machine learning algorithms for enabling scalable and privacy-preserving intelligent healthcare applications would be extremely valuable.