Abstract The next generation of telesurgery demands quantum-resilient, ultra-low latency, and highly secure distributed AI frameworks capable of functioning across heterogeneous clinical sites. Building on prior federated learning-based telesurgery frameworks, this research introduces a Quantum-Enhanced Chaotic Gated Transformer Learning (Q-CGTL) architecture designed for real-time, multi-site surgical procedures in 6G-enabled smart hospital environments. The framework integrates (i) quantum random number generators (QRNGs) for entropy-rich key generation in the chaotic encryption layer, (ii) multi-head chaotic gated transformer (CGT) models optimized for multi-modal data streams (haptics, imaging, vitals), and (iii) 6G spectrum slicing with AI-driven latency control, reducing end-to-end feedback latency to under 1 ms. Additionally, a cross-institutional federated transfer learning module enables privacy-preserving model sharing without raw data exposure. Extensive evaluations using a synthetic Surgical Digital Twin dataset and the CIC-IDS-2023 cyber-attack dataset demonstrate a 99.4% intrusion detection accuracy, a 35% reduction in energy consumption, and near-zero packet loss under adversarial conditions. Statistical security assessments, including NIST entropy tests and post-quantum cryptographic analysis, confirm the robustness of the proposed system against quantum adversaries. This research represents a pivotal advancement towards autonomous, secure, and explainable telesurgery ecosystems capable of scaling across global clinical networks.
Abstract The next generation of telesurgery demands quantum-resilient, ultra-low latency, and highly secure distributed AI frameworks capable of functioning across heterogeneous clinical sites. Building on prior federated learning-based telesurgery frameworks, this research introduces a Quantum-Enhanced Chaotic Gated Transformer Learning (Q-CGTL) architecture designed for real-time, multi-site surgical procedures in 6G-enabled smart hospital environments. The framework integrates (i) quantum random number generators (QRNGs) for entropy-rich key generation in the chaotic encryption layer, (ii) multi-head chaotic gated transformer (CGT) models optimized for multi-modal data streams (haptics, imaging, vitals), and (iii) 6G spectrum slicing with AI-driven latency control, reducing end-to-end feedback latency to under 1 ms. Additionally, a cross-institutional federated transfer learning module enables privacy-preserving model sharing without raw data exposure. Extensive evaluations using a synthetic Surgical Digital Twin dataset and the CIC-IDS-2023 cyber-attack dataset demonstrate a 99.4% intrusion detection accuracy, a 35% reduction in energy consumption, and near-zero packet loss under adversarial conditions. Statistical security assessments, including NIST entropy tests and post-quantum cryptographic analysis, confirm the robustness of the proposed system against quantum adversaries. This research represents a pivotal advancement towards autonomous, secure, and explainable telesurgery ecosystems capable of scaling across global clinical networks.
Future 6G-enabled smart hospital infrastructures will support latency-critical medical operations such as robotic surgery, autonomous monitoring, and real-time clinical decision systems, which require communication mechanisms that ensure both ultra-low latency and long-term cryptographic security. Existing security solutions either rely on classical cryptographic protocols that are vulnerable to quantum attacks or deploy isolated post-quantum primitives without providing a unified framework for secure real-time medical command transmission. This research presents a latency-aware, multi-layered post-quantum security architecture for 6G-enabled smart hospital environments. The proposed framework establishes an end-to-end secure command transmission pipeline that integrates hardware-rooted device authentication, post-quantum key establishment, hybrid payload protection, dynamic access enforcement, and tamper-evident auditing within a coherent system design. In contrast to existing approaches that focus on individual security mechanisms, the architecture introduces a structured integration of Kyber-based key encapsulation and Dilithium digital signatures with hybrid AES-based encryption and legacy-compatible key transport, while Physical Unclonable Function authentication provides hardware-bound device identity verification. Zero Trust access control, metadata-driven anomaly detection, and blockchain-style audit logging provide continuous verification and traceability, while threshold cryptography distributes cryptographic authority to eliminate single points of compromise. The proposed architecture is evaluated using a discrete-event simulation framework representing adversarial conditions in realistic 6G medical communication scenarios, including replay attacks, payload manipulation, and key corruption attempts. Experimental results demonstrate improved security and operational efficiency, achieving a 48% reduction in detection latency, a 68% reduction in false-positive anomaly detection rate, and a 39% improvement in end-to-end round-trip latency compared to conventional RSA-AES-based architectures. These results demonstrate that the proposed framework provides a practical and scalable approach for achieving post-quantum secure and low-latency command transmission in next-generation 6G smart hospital systems.
The rapid evolution of wireless surgical ecosystems, driven by 5G/6G networks, autonomous robotics, AI-assisted decision systems, created unprecedented opportunities for precision medicine. Advancements introduce critical cybersecurity vulnerabilities, especially as emerging quantum computing capabilities threaten to break classical cryptographic foundations. The research examines a holistic framework for securing next-generation surgical environments through post-quantum cryptography, zero-trust architectures, authenticated robotic control channels, and ultra-reliable low-latency communication (URLLC) safeguards. Unlike traditional security evaluations, work integrates empirical findings from contemporary surgical robotics, NIST The chapter emphasizes resilience against quantum-enabled attacks, interoperability challenges in mixed-vendor operating rooms, and the need for continuous cryptographic agility. The result provides a robust foundation for future autonomous surgical platforms where safety, accuracy, and security must coexist within tightly constrained latency margins.
Digital Twin (DT) technology, with Federated Learning (FL), enables decentralised and privacy-aware intelligence of large-scale infrastructures of smart cities. Quantum computing compromises the existing cryptography systems that underlie existing FL frameworks. The current paper presents a Quantum-Secure FL (QSec-FL) architecture that incorporates both Post-Quantum Cryptography (PQC) and Quantum Key Distribution (QKD) as methods to ensure quantum-resistant communication between the IoT, edge, and cloud layers of 6G networks. The framework facilitates real-time DT synchronisation and ultra-reliable low-latency communication (URLLC) through adaptive model aggregation and secure key management. With the help of a large dataset of SmartCity-6G-QSec, QSec-FL features better robustness, synchronisation integrity, and intrusion resilience than traditional secure FL systems. Additionally, the framework is compliant with the standards of NIST PQC, ETSI QKD, and 3GPP Release 19, ensuring compatibility and readiness for use in future 6G-based infrastructures. QSec-FL is a quantum-resilient security system incorporating federated intelligence to establish a unified, dependable, real-time DT operation and safe AI-based decision-making of the advanced smart cities.
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.
AI-native 6G networks are envisioned to tightly embed artificial intelligence (AI) into the wireless ecosystem, enabling real-time, personalized, and privacy-preserving intelligence at the edge. A foundational pillar of this vision is federated learning (FL), which allows distributed model training across devices without sharing raw data. However, implementing classical FL methods faces several bottlenecks in heterogeneous dynamic wireless networks, including limited device compute capacity, unreliable connectivity, intermittent communications, and vulnerability to model security and data privacy breaches. This article investigates the integration of quantum federated learning (QFL) into AI-native 6G networks, forming a transformative paradigm capable of overcoming these challenges. By leveraging quantum techniques across computing, communication, and cryptography within FL workflows, QFL offers new capabilities along three key dimensions: (i) edge intelligence, (ii) network optimization, and (iii) security and privacy, which are studied in this work. We further present a case study demonstrating that a QFL framework employing the quantum approximate optimization algorithm outperforms classical methods in model convergence. We conclude the paper by identifying practical challenges facing QFL deployment, such as quantum state fragility, incompatibility with classical protocols, and hardware constraints, and then outline key research directions toward its scalable real-world adoption.
Sixth-generation (6G) wireless networks will underpin ultra-dense Industrial IoT (IIoT) ecosystems in which resource-constrained Far-Edge devices -- autonomous mobile robots, industrial actuators, connected vehicles -- must simultaneously satisfy sub-millisecond latency, 10−7-class reliability, and decades-long cryptographic security. Current architectures delegate Digital Twin (DT) computation to centralised cloud or Mobile Edge Computing (MEC) servers, incurring prohibitive round-trip latency, and rely on classical public-key cryptography vulnerable to quantum attacks under the harvest-now, decrypt-later (HNDL) threat model. We propose Q-FE, a Quantum-Native 6G Far-Edge architecture integrating three co-designed components: (i) Micro-Digital Twins (μDTs) co-located with 6G base stations and high-capability endpoints; (ii) a Cross-Layer Post-Quantum Key Exchange module embedding CSIDH-512 isogeny key material directly within MAC-layer control frames, exploiting the scheme's uniquely compact keys (≤64 bytes) to avoid packet fragmentation; and (iii) an Asynchronous Federated Learning (AFL) protocol governed by lightweight DAG smart contracts at MEC nodes, eliminating straggler bottlenecks and preventing model-poisoning and Sybil attacks without exposing raw data. End-to-end simulations (NS-3 + PySyft) demonstrate that Q-FE reduces MAC-layer overhead by 62% versus ML-KEM/Kyber-1024, maintains P99.9 URLLC latency at 0.78 ms, and accelerates global-model convergence by 31% over synchronous Federated Learning. Protocol complexity analysis confirms O(NlogR) per aggregation round, and μDT handover migration completes in 1.9±0.3 ms across 104 simulated events. A formal threat model confirms resilience against quantum eavesdropping, model-poisoning, and Sybil attacks.