Deep-space medical surveillance necessitates decentralized AI frameworks that are resilient to communication latency and resource scarcity. This study introduces QFedAstro, which integrates variational quantum circuits with federated learning for astronaut health anomaly detection across heterogeneous mission profiles. The architecture employs five synergistic modules: quantum-classical hybrid optimization (QCHO) navigating non-convex loss landscapes via superposition-inspired parameter updates; adaptive mission intelligence networks (AMIN) reconfiguring layer weights based on gravity-radiation-isolation vectors; temporal-spatial health embedding (TSHE) capturing physiological interdependencies through graph attention; privacy-preserving biometric fusion (PPBF) applying mission-calibrated differential noise (ϵ = 0.5, δ = 10 -5 ); and cross-mission knowledge crystallization (CMKC) enabling inter-environment transfer learning. Validation across 150-astronaut simulations spanning ISS, Lunar, Martian, and Deep Space contexts yields 96.73% accuracy—2.61% above centralized baselines—while achieving 147x privacy amplification and 68% bandwidth reduction. Paired t-tests confirmed significance (p < 0.001, Cohen’s d = 2.41). The framework challenges conventional privacy-utility tradeoffs, demonstrating that quantum augmentation enables the concurrent optimization of fidelity and sovereignty in mission-critical federated systems.
Deep-space medical surveillance necessitates decentralized AI frameworks that are resilient to communication latency and resource scarcity. This study introduces QFedAstro, which integrates variational quantum circuits with federated learning for astronaut health anomaly detection across heterogeneous mission profiles. The architecture employs five synergistic modules: quantum-classical hybrid optimization (QCHO) navigating non-convex loss landscapes via superposition-inspired parameter updates; adaptive mission intelligence networks (AMIN) reconfiguring layer weights based on gravity-radiation-isolation vectors; temporal-spatial health embedding (TSHE) capturing physiological interdependencies through graph attention; privacy-preserving biometric fusion (PPBF) applying mission-calibrated differential noise (ϵ = 0.5, δ = 10 -5 ); and cross-mission knowledge crystallization (CMKC) enabling inter-environment transfer learning. Validation across 150-astronaut simulations spanning ISS, Lunar, Martian, and Deep Space contexts yields 96.73% accuracy—2.61% above centralized baselines—while achieving 147x privacy amplification and 68% bandwidth reduction. Paired t-tests confirmed significance (p < 0.001, Cohen’s d = 2.41). The framework challenges conventional privacy-utility tradeoffs, demonstrating that quantum augmentation enables the concurrent optimization of fidelity and sovereignty in mission-critical federated 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.
Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data, making it suitable for privacy-sensitive robotic sensing applications. However, multi-agent systems generate heterogeneous and non-independent and identically distributed (non-IID) multimodal sensor streams that degrade conventional FL algorithms, while classical fusion modules introduce substantial parameter overhead and communication cost. This paper proposes QFedAgent, a hybrid quantum-classical personalized FL framework for multi-agent activity recognition. The approach integrates a variational quantum circuit fusion module that models accelerometer--gyroscope interactions through quantum state encoding and entanglement, requiring only 72 quantum rotation parameters versus 33K in classical multi-layer perceptron-based fusion, achieving approximately 10x total parameter reduction. Experiments on the OPPORTUNITY dataset under subject-based non-IID partitions demonstrate 97.7% mean test accuracy, confirming that parameter-efficient quantum fusion remains competitive with conventional federated baselines.
Dynamic Radio Resource Allocation (RRA) in Low Earth Orbit-Space Integrated Ground Networks (LEOSIGNs) presents unprecedented challenges due to extreme dynamism, sparse resource availability, and heterogeneous quality-of-service requirements across satellite constellations like Starlink, OneWeb, and Kuiper. This paper introduces a paradigmshifting Quantum-Optimized Deep Learning with Federated Edge Intelligence (QO-FEDI) framework that synergistically integrates: (1) Variational Quantum Circuits (VQC) for decomposing the NP-hard RRA problem into parametric quantum optimization, (2) federated attention-based predictive models across satellite clusters for privacy-preserving demand forecasting, and (3) adaptive edge-compute resource coordination for sub- 100 ms allocation latency. Novel contributions include: mathematical formulation of quantum cost Hamiltonians capturing interference constraints as entanglement patterns, federated learning topology with Byzantine-robust aggregation, and spatio-temporal attention mechanisms for orbital trajectory-aware predictions. Extensive evaluations across 15 realistic LEO scenarios with varying satellite configurations (32-256 satellites), traffic patterns (1−25Gbps), and environmental conditions demonstrate: (1)71%spectrum efficiency improvement (reaching6.2bps/Hz), (2) 58% latency reduction (achieving 8.4 ms average), (3) 82 % handover optimization with & 96 % success rates, and (4) superior scalability to1000+satellite constellations. Comparative analysis with 8 state-of-the-art methods validates near-optimal solution quality (96.1 % vs. theoretical bounds). Real-world testbed deployment on constellation simulator validates practical applicability and deployment readiness for commercial satellite operators.
The rapid growth of industrial Internet of Things (IIoT) systems has created new challenges for anomaly detection in high-dimensional, multivariate time-series, where privacy, scalability, and communication efficiency are critical. Classical federated learning approaches mitigate privacy concerns by enabling decentralized training, but they often struggle with highly non-linear decision boundaries and imbalanced anomaly distributions. To address this gap, we propose a Federated Quantum Kernel Learning (FQKL) framework that integrates quantum feature maps with federated aggregation to enable distributed, privacy-preserving anomaly detection across heterogeneous IoT networks. In our design, quantum edge nodes locally compute compressed kernel statistics using parameterized quantum circuits and share only these summaries with a central server, which constructs a global Gram matrix and trains a decision function (e.g., Fed-QSVM). Experimental results on synthetic IIoT benchmarks demonstrate that FQKL achieves superior generalization in capturing complex temporal correlations compared to classical federated baselines, while significantly reducing communication overhead. This work highlights the promise of quantum kernels in federated settings, advancing the path toward scalable, robust, and quantum-enhanced intelligence for next-generation IoT infrastructures.
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.
Anomaly detection has a significant impact on applications such as video surveillance, medical diagnostics, and industrial monitoring, where anomalies frequently depend on context and anomaly-labeled data are limited. Quantum machine learning (QML) offers powerful tools for effectively processing high-dimensional data, but centralized QML systems face considerable challenges, including data privacy concerns and the need for massive quantum resources at a single node. Quantum federated learning (QFL) overcomes these concerns by distributing model training among several quantum clients, consequently eliminating the requirement for centralized quantum storage and processing. However, in real-life quantum networks, clients frequently differ in terms of hardware capabilities, circuit designs, noise levels, and how classical data is encoded or preprocessed into quantum states. These differences create inherent heterogeneity across clients—not just in their data distributions, but also in their quantum processing behaviors. As a result, training a single global model becomes ineffective, especially when clients handle imbalanced or non-identically distributed ( non-IID) data. To address this, we propose a new framework called personalized quantum federated learning (PQFL) for anomaly detection. PQFL enhances local model training at quantum clients using parameterized quantum circuits and classical optimizers, while introducing a quantum-centric personalization strategy that adapts each client's model to its own hardware characteristics and data representation. This balances local customization with global coordination. Extensive experiments show that PQFL significantly improves anomaly detection accuracy under diverse and realistic conditions. Compared to state-of-the-art methods, PQFL reduces false errors by up to 23%, and achieves gains of 24.2% in AUROC and 20.5% in AUPR, highlighting its effectiveness and scalability in practical quantum federated settings.