ABSTRACT Quantum machine learning (QML) provides the opportunity for the success of an automated medical diagnostic system due to the effective representation of the solution space by quantum entangled states and faster optimisation through quantum superposition. Preserving medical data privacy is crucial while implementing an intelligent and efficient medical service provider system. The federated machine learning model is not only rich in diversified model experiences but also helps to protect patient data privacy. This paper proposes a secure, intelligent Internet of Healthcare Things (IoHT) application with a federated quantum convolutional neural network (FedQCNN) to classify medically significant retinal image patches. Following the gradient descent algorithm, we executed the model optimisation and performed the global aggregation using the weighted average of the local models' parameters. The proposed system achieves an evaluation accuracy of 96.8% on E‐Ophtha retinal image dataset. We suggest over‐the‐air distributed machine learning with wireless multiple access channels to significantly save the scaled‐up radio resource requirements for massive connectivity to ultra dense IoT devices. The research emphasises the significant progress of federated quantum machine learning and its prospects in the coming decades.
ABSTRACT Quantum machine learning (QML) provides the opportunity for the success of an automated medical diagnostic system due to the effective representation of the solution space by quantum entangled states and faster optimisation through quantum superposition. Preserving medical data privacy is crucial while implementing an intelligent and efficient medical service provider system. The federated machine learning model is not only rich in diversified model experiences but also helps to protect patient data privacy. This paper proposes a secure, intelligent Internet of Healthcare Things (IoHT) application with a federated quantum convolutional neural network (FedQCNN) to classify medically significant retinal image patches. Following the gradient descent algorithm, we executed the model optimisation and performed the global aggregation using the weighted average of the local models' parameters. The proposed system achieves an evaluation accuracy of 96.8% on E‐Ophtha retinal image dataset. We suggest over‐the‐air distributed machine learning with wireless multiple access channels to significantly save the scaled‐up radio resource requirements for massive connectivity to ultra dense IoT devices. The research emphasises the significant progress of federated quantum machine learning and its prospects in the coming decades.
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.
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.
Medical image classification is an important task in clinical decision support systems. Although classical deep learning models can represent massive amounts of data, they are not very scalable DEEP LEARNING REVIEW and stand each in their own right for privacy loss and computation inefficiency. Quantum machine learning (QML) has been proposed as an exciting field to complement classical machine learning by using quantum superposition and entanglement for increasing the expressibility of the models. Since the hybrid quantum-classical methods are subject to high qubit complexity [10], training instability (barren plateau problem) [13, 14], non-existent privacy protection mechanisms and poor generalisability over trained datasets [7]. Herein, we propose AF-HQCNN: an Adaptive Federated Hybrid Quantum Convolutional Neural Network (1) a lightweight classical feature extractor using only MobileNetV2, (2) variational quantum circuit (VQC) with angle and amplitude encoding with 4-8 qubits, (3) novel adaptive momentum-based quantum optimizer to mitigate gradient vanishing and gradient explosion issues, (4) federated learning aggregation layer layer to ensure protection of patient data privacy,(5), multi-dataset validation on MedMNIST, HAM10000, Brain MRI & Chest X-Ray datasets. Our experimental results show that AF-HQCNN leads to the mean classification accuracy of 97.6%, outperforming existing hybrid models by a margin of 2.7% with 87% fewer trainable parameters, faster convergence and robustness against privacy attacks.
Abstract In recent years, the concept of federated machine learning has been actively driven by scientists to ease the privacy concerns of data owners. Currently, the combination of machine learning and quantum computing technologies is a hot industry topic and is positioned to be a major disruptor. It has become an effective new tool for reshaping several industries ranging from healthcare to finance. Data sharing poses a significant hurdle for large-scale machine learning in numerous industries. It is a natural goal to study the advanced quantum computing ecosystem, which will be comprised of heterogeneous federated resources. In this work, the problem of data governance and privacy is handled by developing a quantum federated learning approach, that can be efficiently executed on quantum hardware in the noisy intermediate-scale quantum era. We present the federated hybrid quantum–classical algorithm called a quanvolutional neural network with distributed training on different sites without exchanging data. The hybrid algorithm requires small quantum circuits to produce meaningful features for image classification tasks, which makes it ideal for near-term quantum computing. The primary goal of this work is to evaluate the potential benefits of hybrid quantum–classical and classical-quantum convolutional neural networks on non-independently and non-identically partitioned (Non-IID) and real-world data partitioned datasets among several healthcare institutions/clients. We investigated the performance of a collaborative quanvolutional neural network on two medical machine learning datasets, COVID-19 and MedNIST. Extensive experiments are carried out to validate the robustness and feasibility of the proposed quantum federated learning framework. Our findings demonstrate a decrease of 2%–39% times in necessary communication rounds compared to the federated stochastic gradient descent approach. The hybrid federated framework maintained a high classification testing accuracy and generalizability, even in scenarios where the medical data is unevenly distributed among clients.
Alzheimer’s disease (AD) is a progressive neurological disorder and one of the most common types of dementia, causing impaired memory and quality of life. Therefore, an early and accurate diagnosis is essential for intervention and patient care. Convolutional Neural Networks (CNNs) and other deep learning models have demonstrated promise in medical imaging but may need centralized collection of data, which raises privacy concerns. In this study, we propose a Federated Hybrid Quantum Convolutional Neural Network (FH-QCNN) that integrates quantum computing with federated learning for privacy-preserving classification of Alzheimer’s disease through magnetic resonance imaging (MRI). This framework enables collaborative model training across multiple clients without sharing raw patient data. A Parametric Quantum Circuit (PQC) with 4- and 8-qubit configurations is incorporated into the CNN architecture to explore hybrid quantum-classical feature representations. On the evaluated Kaggle benchmark dataset, the 8-qubit FH-QCNN with three clients achieved slice-level classification accuracies of 99.69% for binary classification and 99.02% for multiclass classification. These results demonstrate the methodological feasibility of the proposed hybrid quantum federated framework under the evaluated benchmark protocol rather than its clinical performance. Additional evaluations under heterogeneous Non-Independent and Identically Distributed (non-IID) federated environments, reduced training data settings, and NISQ-inspired noisy quantum conditions demonstrated consistent performance under the evaluated simulation settings. Gradient-weighted Class Activation Mapping (Grad-CAM)-based explainability analysis further indicated that the FH-QCNN focused on anatomically relevant brain regions during prediction.
Federated learning (FL) is a privacy-preserving alternative to centralized machine learning, where model training is performed on local devices and only global model updates are shared, effectively addressing challenges, such as data silos and privacy protection. Recently, quantum FL (QFL), an emerging FL branch, has garnered significant attention in many industry applications. However, existing QFL algorithms predominantly employ average weighting for global model training, which shows poor performance on heterogeneous healthcare data. To address this challenge, this study proposes a dynamic aggregation QFL algorithm (DAQFL) for intelligent diagnosis. Specifically, it utilizes quantum neural networks (QNNs) as local training models and designs corresponding variational quantum circuits (VQC). To mitigate performance degradation caused by the heterogeneity of medical industrial data, a dynamic aggregation method based on accuracy is proposed to enhance global model performance effectively. Extensive experiments with three distribution settings, including independent and identically distributed (IID), non-independent and identically distributed (Non-IID), and long-tail datasets, show that DAQFL outperforms baseline algorithms in accuracy and training speed. It also performs well in privacy protection and robustness of anti-noise, improving its suitability for real-world medical applications.