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arXiv:2606.03611

Q-FE: A Quantum-Native 6G Far-Edge Architecture Securing Industrial IoT Digital Twins via CSIDH-PQC and Asynchronous Federated Learning

Vincenzo Sammartino

Published Jun 2, 2026• 0 citations
arXivHTMLPDF

Abstract

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 ($μ$DTs) 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 ($\le 64$ 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 $O(N \log R)$ per aggregation round, and $μ$DT handover migration completes in $1.9 \pm 0.3$ ms across $10^4$ simulated events. A formal threat model confirms resilience against quantum eavesdropping, model-poisoning, and Sybil attacks.

DOI
10.48550/arxiv.2606.03611

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The convergence of quantum computing and the scale of Internet of Things (IoT) deployments creates a compounded security problem: classical public-key cryptography securing today’s IoT traffic is vulnerable to harvest-now, decrypt-later attacks, while the constrained hardware typical of IoT devices limits the security mechanisms can be deployed. Prior work has largely evaluated post-quantum cryptography (PQC), federated learning (FL)-based anomaly detection, and quantum-resilient storage separately or in partial combinations, leaving open whether these mechanisms remain practical when composed on constrained hardware under a shared operational budget. This paper answers that question with a three-layer architecture — a hybrid PQC pipeline (Kyber-based key encapsulation, ML-DSA signatures, AES-GCM encryption, and PQC-TLS transport), a federated anomaly-detection layer, and a quantum-resilient storage and audit layer (DeSSE and MuReQua) — executed end-to-end on a Raspberry Pi 5 edge node under AWS IoT Greengrass Core, with nine concurrent virtual IoT nodes processing real attack traffic from the N-BaIoT and TON_IoT datasets. Benchmarking across 27 cryptographic algorithm combinations (810 pipeline executions) shows that all 27 configurations completed in under 2.1 ms, with the deployed Kyber768/ML-DSA-65/AES-256-GCM tier adding 4,521 bytes of per-message overhead, which remained compatible with the evaluated 20 ms inter-row transmission budget. Live federated deployment across the nine virtual IoT nodes on N-BaIoT achieves F1-scores above 0.998 on eight of nine devices, processing at approximately 900 rows/second with zero signature verification failures; a centralized baseline evaluation further confirms 99.46% attack recall on unseen TON_IoT threats withheld from training. The quantum-resilient storage layer maintains 100% chain integrity and complete signature coverage across 4,471 audit records, with independent tamper simulations reliably detected. PQC-TLS introduces a one-time handshake overhead of 13.7 ms (288.5% relative increase) over classical TLS, which remains small relative to the multi-minute FL round intervals measured in the evaluated deployment. Together, these results demonstrate that quantum-resilient cryptography, federated intelligence, and auditable storage can be jointly executed on constrained IoT edge hardware as a single experimentally demonstrated pipeline within the operational conditions evaluated in this study.
Mission-critical IoT applications in sixth-generation (6G) networks demand decoding error probability of 10−9, latency under 1 ms, and quantum-resistant security. This paper proposesQFL-SecEdge, a quantum federated learning framework integrating post-quantum cryptography for secure, ultra-reliable task distribution in 6G edge computing. QFL-SecEdge features a three-tier architecture comprising: Variational Quantum Circuit-enhanced Federated Deep Reinforcement Learning (VQC-FDRL) with 45% faster convergence; a Lattice-based Post-Quantum Cryptographic (LPQC) layer using CRYSTALS-Kyber with 78% reduced overhead; and an Extreme Value Theory-driven Reliability Assurance (EVT-RA) mechanism guaranteeing 99.99999% reliability. Simulations demonstrate 52% energy efficiency improvement, 38% latency reduction, 10−9decoding error probability, and quantum-resistant security at only 1.3× computational overhead. The reported gains are based on simulation using quantum circuit emulation, simulated 6G IoT devices, and software-based Kyber implementation; results may vary under real-world deployment conditions.
Abstract The transition to sixth-generation (6G) wireless networks demands cryptographic frameworks that simultaneously satisfy sub-millisecond latency constraints and resilience against quantum-capable adversaries; requirements that neither conventional quantum key distribution (QKD) nor emerging post-quantum cryptographic (PQC) schemes adequately address in isolation. This paper presents the Quantum Burst Protocol (QBP), a novel quantum-lite encryption scheme that replaces traditional two-way QKD handshakes with stateless, one-way quantum entropy bursts, enabling submillisecond secure communication between mobile and edge devices with minimal quantum hardware (2-3 qubits). QBP introduces three principal innovations: (i)~stateless operation via transient quantum entropy extraction, eliminating continuous channel negotiation; (ii)~ephemeral key derivation through domain-separated one-way hash functions, providing built-in forward secrecy and resistance to harvest-now-decrypt-later attacks; and (iii)~optional quantum-authenticatable message tagging for integrity verification. I present a comprehensive protocol specification encompassing system architecture, cryptographic design, and formal security proofs under quantum adversary models. Performance evaluation demonstrates end-to-end latency of 0.15-0.20\,ms, well within 6G thresholds. A rigorous comparative analysis against QKD (BB84, E91) and NIST-standardised PQC algorithms (ML-KEM, ML-DSA) reveals that QBP uniquely occupies a design space combining quantum-derived entropy strength with symmetric-key efficiency. I further discuss deployment considerations for edge, IoT, and non-terrestrial network scenarios, and outline a roadmap for experimental validation on constrained 6G-class hardware.
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.
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.