Quantum-Enhanced Federated Learning for Real-Time Medical Image Analysis with Ethical AI Governance is a nascent approach combining the principles of federated learning and quantum computing to transform the medical image analysis sector with due regard to the most critical ethical principles. Reflecting on the definition, federated learning is an approach that allows several institutions to jointly train machine learning models without the need to share any patient's data. Therefore, FL is the industry-institution strategic tool that allows for a higher level of privacy security in healthcare. On the other hand, by embracing quantum computing, the developed approach includes more sophisticated computational skills, permitting improved data performance and enhanced model accurateness, a pivotal factor for real-time medical diagnosis. Consequently, the significance of the approach lies in the potential to boost the medical image analysis sector. Quantum-enhanced FL will help comply with the most rigid ethical requirements by involving decentralized data performance and exploiting various datasets characteristic of healthcare providers. This will be particularly pivotal since the current state deems quick and precise medical decisions as of the essence, precisely when imaging tools are developed for early disease detection and recognition. Additionally, the integration presents an opportunity to address the challenges naturally associated with FL, i.e., model accurateness and data heterogeneities. Therefore, quantum algorithms are incorporated to perform training in much more optimized ways than classical alternatives. However, ethical AI governance will remain challenging with the current FL-quantum integration. As the type continues developing, the issues will concern AI consideration, algorithmic perfidies, and accountable decision types so that proper directives are in line. Research continues to advance, but stakeholders must address these ethical considerations to fully leverage the promise of quantum-enhanced federated learning to revolutionize medical image analysis. Thus, a quantum-fed network of neural networks can lead the way in developing solutions that combine the efficiency of quantum computing and the importance of ethical approaches in data management. Responsible and effective utilizing this technology requires continued collaboration between researchers, healthcare providers, and policymakers. "Simulations show our quantum FL model improves tumor segmentation accuracy by 12% (Dice score) over classical federated learning while maintaining stronger privacy guarantees."
Quantum-Enhanced Federated Learning for Real-Time Medical Image Analysis with Ethical AI Governance is a nascent approach combining the principles of federated learning and quantum computing to transform the medical image analysis sector with due regard to the most critical ethical principles. Reflecting on the definition, federated learning is an approach that allows several institutions to jointly train machine learning models without the need to share any patient's data. Therefore, FL is the industry-institution strategic tool that allows for a higher level of privacy security in healthcare. On the other hand, by embracing quantum computing, the developed approach includes more sophisticated computational skills, permitting improved data performance and enhanced model accurateness, a pivotal factor for real-time medical diagnosis. Consequently, the significance of the approach lies in the potential to boost the medical image analysis sector. Quantum-enhanced FL will help comply with the most rigid ethical requirements by involving decentralized data performance and exploiting various datasets characteristic of healthcare providers. This will be particularly pivotal since the current state deems quick and precise medical decisions as of the essence, precisely when imaging tools are developed for early disease detection and recognition. Additionally, the integration presents an opportunity to address the challenges naturally associated with FL, i.e., model accurateness and data heterogeneities. Therefore, quantum algorithms are incorporated to perform training in much more optimized ways than classical alternatives. However, ethical AI governance will remain challenging with the current FL-quantum integration. As the type continues developing, the issues will concern AI consideration, algorithmic perfidies, and accountable decision types so that proper directives are in line. Research continues to advance, but stakeholders must address these ethical considerations to fully leverage the promise of quantum-enhanced federated learning to revolutionize medical image analysis. Thus, a quantum-fed network of neural networks can lead the way in developing solutions that combine the efficiency of quantum computing and the importance of ethical approaches in data management. Responsible and effective utilizing this technology requires continued collaboration between researchers, healthcare providers, and policymakers. "Simulations show our quantum FL model improves tumor segmentation accuracy by 12% (Dice score) over classical federated learning while maintaining stronger privacy guarantees."
Abstract The healthcare industry frequently handles sensitive and proprietary data, and due to strict privacy regulations, it is often reluctant to share it 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 propose a federated learning framework based on quantum tensor networks (QTNs) that takes advantage of the principles of many-body quantum physics. Currently, there are no known classical tensor networks (TNs) 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 (FedQTNs) model achieved a mean receiver-operator characteristic area under the curve of 91%–98%, outperforming several state-of-the-art federated learning methods. Moreover, QTN models require fewer parameters in FL settings compared to traditional classical models, which often suffer from over-parameterization. This reduction in parameters not only improves the efficiency of the communication process but also significantly decreases data consumption during training. As a result, QTN models facilitate a more effective and resource-efficient approach to training in decentralized environments with limited communication bandwidth. The FedQTN models demonstrate a smaller performance drop even when using strong differential privacy settings, maintaining higher accuracy compared to classical models under similar privacy constraints. Experimental results demonstrate that the quantum federated global model, consisting of highly entangled TN 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.
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.
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.
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 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.
AI is a normal practice in the field of medicine. Neural data applications in brain-related AI systems such as neural computer interfaces and the analysis of brain diseases have neural data as input. This kind of information is rather personal and confidential. Because of this, the issue of privacy and information safety is of high priority. The federated learning will help reduce such risks as training of AI models in many different devices or hospitals and it does not involve the flow of raw patient information. Although this solution has improved the privacy, there remain several problems with the traditional federated learning. They include the high costs of communication, lack of privacy protection, and failure to process deep and uneven brain data. Quantum machine learning offers to some extent help in overcoming these challenges. Quantum tensors Networks can code complex data using fewer parameters. This will improve learning efficiency. When quantum tensor networks are deployed, quantum-enhanced federated learning can be utilized in order to deliver more secure and privacy-conscious neuro-AI systems. The combination of these approaches in the healthcare practices is discussed only superficially in this review. It outlines the central ideas, system architecture and privacy. Other topical issues, such as the insufficiency of quantum equipment and the impossibility to transfer the developed practice to practice, are also covered in the review. Overall, one can observe that this paper will help the researchers to understand this emerging discipline and support further study of the subject of safe and confidential neuro-AI systems.