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
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
The exponential growth of bioinformatics data, including genomic sequences and protein structures, necessitates advanced analytical frameworks that ensure privacy, computational efficiency, and scalability. To address these challenges, we propose an integrated framework that combines Federated Learning (FL) with Quantum Computing (QC) for secure and efficient bioinformatics data analysis across distributed healthcare institutions. The proposed Quantum Federated Learning (QFL) architecture employs variational quantum circuits to expedite local model training, achieving a 35% reduction in computation time compared to classical FL methods. To strengthen security, Quantum Secure Communication Protocols (QSCP) are incorporated, resulting in over 40% enhanced resilience against adversarial attacks relative to conventional encryption schemes. Experimental validation on distributed genomic datasets, such as the Cancer Genome Atlas, demonstrates that QFL attains a classification accuracy of 94.8% in gene expression-based cancer subtype prediction, surpassing traditional FL by 6.3%. Furthermore, the model exhibits a 25% reduction in communication overhead through quantum state compression, ensuring efficient data exchange. This hybrid approach effectively addresses the critical requirements of data privacy, computational acceleration, and model robustness in bioinformatics, establishing a promising pathway for secure and scalable precision medicine solutions empowered by quantum-enhanced federated learning.
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 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.
The exponential growth of genomic data has created opportunities for collaborative biomedical research while posing significant privacy and security challenges. Traditional centralized machine learning approaches risk data breaches and regulatory non-compliance. This study proposes a novel hybrid architecture combining Federated Quantum Machine Learning (FQML) with Differential Privacy (DP) for secure multi-party genomic data analysis. Our simulated framework enables institutions to collaboratively train quantum machine learning models on distributed genomic datasets without exposing sensitive data. Using variational quantum circuits within a federated learning framework, we implement privacy-preserving models across three simulated genomic data silos. Gaussian noise injection via DP mechanisms during gradient updates ensures formal privacy guarantees (ε = 0.85). Experimental results on synthetic datasets demonstrate that FQML achieves 93.4% predictive accuracy in genome-wide association studies simulation, outperforming classical federated models in convergence speed by 27% and reducing communication overhead by 19%. These findings suggest FQML with DP provides a promising foundation for future secure genomic collaboration, though real-world validation remains necessary.
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.
The increasing availability of electronic health records, medical imaging databases, and sensor-generated clinical datasets has boosted the need for privacy-preserving yet scalable analytical frameworks. In this regard, quantum computing and federated learning are becoming two focal points that may significantly shape future healthcare data processing. Quantum computing provides powerful tools for working with complex, high-dimensional optimization and learning problems, while federated learning facilitates collaborative model training without requiring the centralized exchange of sensitive information about patients. This review explores the integration of these two technologies and Quantum Split Federated Learning (QSFL) as a promising approach for secure and scalable co-operative health care analytics. The treatment then turns to discuss the quantum computation mathematical and algorithmic basics, and subsequently, quantum machine learning methods based on extremes of supervised, unsupervised, and semi-supervised modes. Special emphasis is placed on how QSFL can enable privacy-aware inter-institutional learning and enhance clinical decision support systems. It also surveys the applications for creating high-quality synthetic medical data sets for training, validation and data-sharing using quantum generative adversarial networks (qGANs). In addition to the technological opportunities this paper critically evaluates the practical challenges to real-world implementation, such as hardware limitations, communication costs, regulatory matters, and ethical considerations of a distributed medical learning system-based infrastructure and mechanism. In the end, the survey identifies important open research problems and provides suggestions for future research directions towards realizing quantum-enabled, privacy-preserving healthcare intelligence systems that are anticipated to usher in a new era of medical analytics.