QFL-SecEdge: A Quantum Federated Learning Framework With Postquantum Cryptographic Task Distribution for Ultrareliable Mission-Critical 6G-IoT Networks
Mohammed Ali Yaseen, Fayu Wan, Sania Asif, Jukun Wan
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.
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.
Quantum federated learning (QFL) is emerging as a key enabler for intelligent, secure, and privacy-preserving model training in next-generation 6G networks. By leveraging the computational advantages of quantum devices, QFL offers significant improvements in learning efficiency and resilience against quantum-era threats. However, future 6G environments are expected to be highly dynamic, decentralized, and data-intensive, which necessitates moving beyond traditional centralized federated learning frameworks. To meet this demand, blockchain technology provides a decentralized, tamper-resistant infrastructure capable of enabling trustless collaboration among distributed quantum edge devices. This paper presentsQFLchain, a novel framework that integrates QFL with blockchain to support scalable and secure 6G intelligence. In this work, we investigate four key pillars ofQFLchainin the 6G context: 1) communication and consensus overhead; 2) scalability and storage overhead; 3) energy inefficiency; and 4) security vulnerability. A case study is also presented, demonstrating potential advantages ofQFLchain, based on simulation, over state-of-the-art approaches in terms of training performance.
Immersive and mission-critical data-driven applications, such as virtual or augmented reality, tactile Internet, industrial automation, and autonomous mobility, are creating unprecedented challenges for ultra-reliable and low-latency communication (URLLC) in the sixth generation (6G) networks. Machine intelligence approaches deep learning, reinforcement learning, and federated learning (FL), to provide new paradigms to ensure 6G URLLC on the stream of big data training. However, classical limitations of machine learning capabilities make it challenging to achieve stringent 6G URLLC requirements. In this article, we investigate the potential of variational quantum computing and quantum machine learning (QML) for 6G URLLC by utilizing the advantage of quantum resources, such as superposition, entanglement, and quantum parallelism. The underlying idea is to integrate quantum machine intelligence with 6G networks to ensure stringent 6G URLLC requirements. As an example, we demonstrate the quantum approximate optimization algorithm for NP-hard URLLC task offloading optimization problems. The variational quantum computation for QML is also adopted in wireless networks to enhance the learning rate of machine intelligence and ensure the learning optimality for mission-critical applications. Considering the security and privacy issues, as well as computational-resource overheads in FL, distributed quantum computation in blind and remote fashions is further investigated for quantum-assisted FL.
Unmanned Aerial Vehicles (UAVs), as key enablers of 6G-enabled Internet of Things (IoT) ecosystems, facilitate dynamic aerial coverage, seamless edge intelligence, and adaptive routing. However, despite these advantages, the reliability and trustworthiness of UAV swarms in 6G remain critical concerns due to rising quantum threats and limitations of traditional machine learning approaches. The paper presents QuaRTA-6G, a Quantum-Resilient, Trust-aware, and Accountable framework that provides a unified solution for 6G UAV swarms. QuaRTA-6G achieves security, trust, and efficiency by seamlessly combining post-quantum cryptography for secure communication, a decentralized ledger for identity and trust management, and Variational Quantum Federated Learning (VQFL) for efficient swarm intelligence. For quantum-resistant authentication and immutable UAV identity verification, QuaRTA-6G leverages CRYSTALS-Kyber with blockchain. Through simulations, QuaRTA-6G achieves secure authentication handshakes in under 2 ms and scales robustly to swarms of more than 100 UAVs, keeping high integrity even under 20% packet loss. Experiments demonstrate that, even under strong data poisoning attacks, the framework achieves 92% accuracy and 85% mission success rate, while comprehensive resource analysis confirms its feasibility across both standard and resource-constrained UAV platforms. Furthermore, an ablation study demonstrates that each module of QuaRTA-6G is essential for ensuring a responsible and trustworthy 6G UAV framework.
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.
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.
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.