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.
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.
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.
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.
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.
Skin cancer, in particular melanoma, is still one of the most significant contributing factors to cancer-related deaths globally, indicating the urgency of reliable diagnostic tools. Early discovery of skin lesions can radically increase patient survival rates, but manual diagnosis is not an easy task, and it is time-consuming as well. This paper uses Quantum Convolutional Neural Networks (QCNN), a hybrid quantum-classical model for the skin lesion classification task as being benign or malignant. The model intertwines quantum feature extraction using an amplitude encoding and parameterized quantum circuit with classical convolutional neural network (CNN) layers analyzing spatial features. Advanced pre-processing techniques and Synthetic Minority Over-sampling Techniques (SMOTE) address the class imbalance, as federated learning ensures decentralized nodes’ privacy-preserving training. The explainability of the model predictions is heightened through Quantum SHAP (Shapley Additive Explanations) and Gradient-Weighted Class Activation Mapping (Grad-CAM). This work evaluates QCNN on the Melanoma Skin Cancer Dataset of 10,600 images, achieving 96.87% training accuracy and a validation accuracy of 85% while surpassing classical benchmarks in accuracy, efficiency, and robustness. Therefore, this quantum-classical model has highlighted potential applications of medical image diagnostics through quantum-classical models
Quantum Federated Learning (QFL) inherits the core vulnerability of federated optimization to malicious clients, while also introducing an attack surface from variational circuit training and measurement-driven gradients. This work proposes a novel CircUit-Level backdoor Threat (CULT) model that formalizes four stealthy attacks by exploiting quantum-aware mechanisms, including Grover, Pauli, Bit-flip, and Sign-flip. By enabling malicious clients on both in-training and post-training surfaces, these attacks can critically undermine the learning process. We establish a rigorous theoretical foundation to demonstrate attack stealthiness under standard smoothness assumptions. Experiments on the MNIST and CIFAR-10 datasets with non-IID splits and varying fractions of malicious clients show that even a single malicious client can induce severe accuracy degradation under FedAvg aggregation. While popular defenses, including Krum, Multi-Krum, FoolsGold, FLGuardian, and Mud-HoG, reduce degradation in many regimes, they fail to eliminate worst-case failure cases, where accuracy drops up to 50\%. The experimental analysis further reveals that under the CULT model, malicious updates effectively mask their presence by staying close to benign norms, thereby helping attackers evade detection.
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.