The growing prevalence of mental stress has created a strong need for intelligent systems capable of early and accurate detection. This work proposes a hybrid learning framework that combines classical machine learning, quantum computing techniques, and federated learning to classify stress levels in in-dividuals. Initially, physiological signalsfrom the WESAD dataset are processed to remove noise and normalize feature values. A Random Forest model is then applied to identify the most influential features, which helps reduce data dimensionality and makes the model suitable for quantum processing. The selected features are transformed into quantum states using rotation-based angle encoding with RY gates. A variational quantum neural network is designed to learn patterns within the encoded data through parameterized quantum circuits. To ensure data privacy and enable decentralized training, a federated learning strategy is incorporated, where multiple clients train models locally and share only model parameters. These parameters are combined using a federated averaging method to form a global model without exposing sensitive data. The developed system categorizes mental stress into three levels: low, medium, and high. The results indicate that integrating quantum learning with federated approaches can effectively handle sensitive health data while maintaining reliable classification performance. This study demonstrates the potential of combining emerging computational paradigms to build secure and efficient healthcare prediction systems.
The growing prevalence of mental stress has created a strong need for intelligent systems capable of early and accurate detection. This work proposes a hybrid learning framework that combines classical machine learning, quantum computing techniques, and federated learning to classify stress levels in in-dividuals. Initially, physiological signalsfrom the WESAD dataset are processed to remove noise and normalize feature values. A Random Forest model is then applied to identify the most influential features, which helps reduce data dimensionality and makes the model suitable for quantum processing. The selected features are transformed into quantum states using rotation-based angle encoding with RY gates. A variational quantum neural network is designed to learn patterns within the encoded data through parameterized quantum circuits. To ensure data privacy and enable decentralized training, a federated learning strategy is incorporated, where multiple clients train models locally and share only model parameters. These parameters are combined using a federated averaging method to form a global model without exposing sensitive data. The developed system categorizes mental stress into three levels: low, medium, and high. The results indicate that integrating quantum learning with federated approaches can effectively handle sensitive health data while maintaining reliable classification performance. This study demonstrates the potential of combining emerging computational paradigms to build secure and efficient healthcare prediction systems.
Mental health disorders represent a growing global healthcare challenge affecting hundreds of millions of people worldwide. Major depressive disorder (MDD), anxiety disorders, post-traumatic stress disorder (PTSD), and Alzheimer’s disease place huge pressure on medical systems. A central challenge is early detection, which remains difficult due to the complexity of symptoms related to these diseases. Delayed diagnosis is also due to limited access to specialists and the multimodal complexity of relevant biomarkers, clinical assessments, neuroimaging, physiological signals, and behavioral data. Recent developments in artificial intelligence (AI) have demonstrated promising results in improving diagnostic support. However, classical AI still struggles to model high-dimensional correlations and inherent uncertainties that are common to cognitive-emotional data. This chapter explores quantum machine learning (QML) as a transformative paradigm to overcome these limitations. We provide a comprehensive overview of foundational QML architectures and evaluate its suitability for processing diverse healthcare data such as neuroimaging, wearable sensor inputs, and electronic health records (EHRs). We propose a new conceptual framework for a quantum-enabled clinical decision support system that integrates multimodal feature extraction. Taking into consideration the sensitivity of psychiatric data, we critically examine security imperatives that include federated learning and post-quantum privacy protection. This chapter aims to provide researchers and practitioners with a comprehensive perspective on how quantum computing (QC) may contribute to future intelligent mental healthcare.
The growing occurrence of patients with multiple chronic diseases has increased the need for efficient multidisease risk prediction systems in healthcare. However, clinical data is often distributed in various cloud, but it lacks in efficient privacy preservation. To address these challenges, this project proposes a hybridized framework for privacy-aware multi-disease risk prediction in distributed healthcare environments. nA key challenge in multi-disease data collection from heterogeneous clinical data may lag in normalization. So, cleaning and preprocessing is done on the first phase. Second challenge lies in capturing complex and non-linear relationships among clinical features within reduced feature spaces. To handle this, quantum-inspired feature encoding and variational quantum circuits are employed to enhance feature representations. Since data is distributed across cloud data privacy and confidentiality issues exist. To overcome this issue Federated Learning for multi-disease prediction is applied in the third phase. To address these challenges, this paper proposes a novel framework titled Hybridized Quantum Computing and Federated Learning for Multi-Disease Prediction (HQCF-MDRP). The proposed system integrates structured data preprocessing, quantum-assisted feature representation, and federated collaborative learning into a unified architecture. Initially, healthcare datasets are cleaned, normalized, and subjected to feature selection to improve data quality and reduce dimensionality. Selected features are then transformed using quantum-inspired angle encoding and Variational Quantum Circuits (VQC), enabling enhanced representation of hidden correlations among clinical attributes. Subsequently, Federated Learning with FedProx optimization is employed to train predictive models collaboratively across distributed healthcare institutions without sharing raw patient data.
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.
Privacy-preserving approaches are essential in health- care applications where sensitive data is involved. Federated learning (FL) has emerged as a widely adopted approach for collaboratively training decentralized models without sharing individual health records. However, ensuring privacy in health- care data, both during training and when clients exchange their models with a central server, remains a challenge. Bias, fairness, clients heterogeneity, and constrained computation are also challenging factors. To address this challenge, in this paper, a communication-efficient and privacy-preserving hybrid Federated learning (HFL) framework is specifically designed for mental healthcare applications. Two HFL approaches, namely Clustered Federated Learning (CFL) and Quantum Federated Learning (QFL), have been proposed. CFL focuses on leveraging the learning behaviour of clients and Conversely, QFL introduces a new phase to FL by incorporating a variational quantum classifier (VQC) for classification tasks. Angle encoding is used for a quantum state preparation to enhance data encoding and learning the quantum model. Experiments were conducted on independent and identically distributed (iid) and non-independent and identically distributed (non-iid) data to evaluate the performance of the proposed methods with state-of-the-art results available in the literature. The results demonstrate exceptional performance in the case of QFL, achieving an accuracy of 84.00%. CFL also exhibits promising results with an accuracy of 78.396%. Additionally, QFL achieves 18.75% better recall and CFL has 6.24% better precision than traditional FL. Nevertheless, it’s crucial to remember that every model has advantages and disadvantages of its own.
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.
Abstract Currently, data privacy and security are critical in deep learning, especially with biometric data. Federated learning (FL) addresses these concerns, but struggles with security, complexity, and high-dimensional data. Quantum Federated Learning (QFL) overcomes these issues by combining FL with quantum computing, thereby offering greater power and parallelism. In this paper, we propose an innovative QFL approach with a quantum–classical hybrid architecture to address the limitations of classical FL and achieve superior performance in video and image classification. The proposed architecture consists of a classical server and quantum–classical clients. On the client side, after preprocessing the videos and images, a pre-trained model extracts high-level features, which are then classified by a client-specific quantum circuit using a Variational Quantum Circuit (VQC). To maximize the performance of this hybrid system, we jointly optimize the VQC parameters and the weights of the pre-trained model’s final feature-extraction layers. The proposed approach has been extensively evaluated in a simulation environment with random client selection to account for the heterogeneity of real-world systems. Furthermore, we present results obtained from quantum computers using globally optimized VQC parameters. This successful validation on actual quantum hardware underscores the practical applicability of our approach, extending its significance beyond theoretical contributions to next-generation applications such as smart campus security.