Title: Adversarially Robust Federated Quantum Machine Learning for Healthcare: Attacks, Adaptive Defenses, and Post-Quantum Secure Aggregation Track: AI for Science Federated quantum machine learning (FQML) offers a compelling paradigm for privacy-preserving clinical AI, enabling distributed training of variational quantum circuit (VQC) models across hospital networks without centralizing sensitive patient records. While prior work has demonstrated the efficiency and accuracy of FQML frameworks, the security of these systems under adversarial conditions remains entirely unaddressed, a gap explicitly identified in foundational FQML research. An empirical adversarial security evaluation of FQML in a healthcare setting is presented here, directly addressing the challenge of building reliable and trustworthy AI systems identified as a central theme of NYSDS 2026. Hybrid VQC clients are implemented using PennyLane on the liver cirrhosis staging dataset distributed across twenty simulated hospital nodes under realistic non-IID Dirichlet partitioning. The system is subjected to five attack types including data poisoning, model poisoning, backdoor injection, man-in-the-middle interception, and a novel adaptive adversary escalation scenario. Protection is provided by a multi-layered adaptive defense stack comprising exponential moving average trust scoring, multi-signal anomaly detection using z-score, variance, and kurtosis signals, adaptive trimmed mean aggregation, per-client differential privacy scheduling, and CRYSTALS-Kyber-768 post-quantum key encapsulation implemented as ML-KEM-768 under NIST FIPS 203 for gradient transmission security. Across three independent experimental runs, the proposed defended system achieves 82.84% mean test accuracy and a security detection AUC of 0.96, compared to 36.0% accuracy for undefended quantum FL under identical adversarial conditions, confirming that FQML without defense is functionally non-operational in adversarial healthcare environments. The defended quantum system AUC of 0.96 exceeds the classical defended baseline of 0.94, demonstrating that classical adaptive defenses transfer effectively to VQC parameter updates and produce superior anomaly detection. Backdoor and model poisoning detection rates reach 100% and 98% respectively. A novel finding identifies a critical operating window of 4 to 12 federated clients within which the system achieves stable performance, with direct implications for practical hospital network deployment. Post-quantum encryption via Kyber-768 provides provable forward security against harvest-now-decrypt-later threats while demonstrably improving man-in-the-middle detection rates. These results establish that adversarially robust federated quantum ML is achievable and motivate the design of quantum-native defense mechanisms for next-generation clinical AI systems.
Title: Adversarially Robust Federated Quantum Machine Learning for Healthcare: Attacks, Adaptive Defenses, and Post-Quantum Secure Aggregation Track: AI for Science Federated quantum machine learning (FQML) offers a compelling paradigm for privacy-preserving clinical AI, enabling distributed training of variational quantum circuit (VQC) models across hospital networks without centralizing sensitive patient records. While prior work has demonstrated the efficiency and accuracy of FQML frameworks, the security of these systems under adversarial conditions remains entirely unaddressed, a gap explicitly identified in foundational FQML research. An empirical adversarial security evaluation of FQML in a healthcare setting is presented here, directly addressing the challenge of building reliable and trustworthy AI systems identified as a central theme of NYSDS 2026. Hybrid VQC clients are implemented using PennyLane on the liver cirrhosis staging dataset distributed across twenty simulated hospital nodes under realistic non-IID Dirichlet partitioning. The system is subjected to five attack types including data poisoning, model poisoning, backdoor injection, man-in-the-middle interception, and a novel adaptive adversary escalation scenario. Protection is provided by a multi-layered adaptive defense stack comprising exponential moving average trust scoring, multi-signal anomaly detection using z-score, variance, and kurtosis signals, adaptive trimmed mean aggregation, per-client differential privacy scheduling, and CRYSTALS-Kyber-768 post-quantum key encapsulation implemented as ML-KEM-768 under NIST FIPS 203 for gradient transmission security. Across three independent experimental runs, the proposed defended system achieves 82.84% mean test accuracy and a security detection AUC of 0.96, compared to 36.0% accuracy for undefended quantum FL under identical adversarial conditions, confirming that FQML without defense is functionally non-operational in adversarial healthcare environments. The defended quantum system AUC of 0.96 exceeds the classical defended baseline of 0.94, demonstrating that classical adaptive defenses transfer effectively to VQC parameter updates and produce superior anomaly detection. Backdoor and model poisoning detection rates reach 100% and 98% respectively. A novel finding identifies a critical operating window of 4 to 12 federated clients within which the system achieves stable performance, with direct implications for practical hospital network deployment. Post-quantum encryption via Kyber-768 provides provable forward security against harvest-now-decrypt-later threats while demonstrably improving man-in-the-middle detection rates. These results establish that adversarially robust federated quantum ML is achievable and motivate the design of quantum-native defense mechanisms for next-generation clinical AI systems.
In this study, A Quantum-Classical Federated Learning (QCFL) framework designed for skin cancer prediction is presented. It integrates classical convolutional neural networks with variational quantum circuits so that both quantum computing and deep learning can be used in decentralized, privacy-preserving environments. The functioning of the Federated Learning model includes a efficient optimization algorithm suitable for quantum environments in FL called SCAFFOLD, which handle client heterogeneity and non-IID data distributions modeled using Dirichlet sampling. The framework includes three ways of training normal training along with trigger-based and semantic-based backdoor attacks to understand demonstrating the susceptibility of federated skin cancer models to stealthy adversarial behaviors. To defend against such threats, multiple detection and mitigation strategies are evaluated, including Activation Clustering, Neural Cleanse, and Fine-Pruning. . Additionally, a novel approach for defense, Spectral Defense, is proposed and developed, based on the eigen-spectrum analysis of internal neural activations to detect and ultimately suppress backdoor behavior without recourse to clean validation data. Experimental results on this hybrid quantum-classical model prove improvement in classification performance, whereas the Spectral Defense framework provides significant attack success rates reduction with little degradation in model accuracy. This work provides a robust and scalable approach for deploying secure federated learning systems to adverse conditions for medical image analysis.
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.
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.
The increasing digitization and decentralization of modern energy systems have heightened their vulnerability to sophisticated cyber threats, necessitating advanced, scalable, and privacy-preserving detection frameworks. This paper introduces a novel Federated Quantum Machine Learning (FQML) framework tailored for anomaly detection in multi-agent energy environments. By integrating parameterized quantum circuits (PQCs) at the local agent level with secure federated learning protocols, the framework enhances detection accuracy while preserving data privacy. A trimmed-mean aggregation scheme and differential privacy mechanisms are embedded to defend against Byzantine behaviors and data-poisoning attacks. The problem is formally modeled as a constrained optimization task, accounting for quantum circuit depth, communication latency, and adversarial resilience. Experimental validation on synthetic smart grid datasets demonstrates that FQML achieves high detection accuracy (≥96.3%), maintains robustness under adversarial perturbations, and reduces communication overhead by 28.6% compared to classical federated baselines. These results substantiate the viability of quantum-enhanced federated learning as a practical, hardware-conscious approach to distributed cybersecurity in next-generation energy infrastructures.
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.