ABSTRACT Background Federated learning (FL) has emerged as a promising paradigm for distributed model training without centralizing raw data, yet it remains vulnerable to gradient leakage, inference attacks, and malicious updates. The integration of quantum technologies into FL, as proposed by Li et al. through two protocols for quantum federated learning (QFL), aims to mitigate these risks. However, a detailed cryptanalysis of their secure inner‐product estimation and incremental learning protocols reveals critical weaknesses, including susceptibility to replay attacks, quantum tomography, entanglement manipulation, and gradient inversion. These vulnerabilities become particularly severe in mission‐critical environments such as military communication networks, where compromised information may directly expose strategic operations and intelligence. Objective To address these limitations, we propose MilQAuth, an improved quantum‐resistant authentication framework for QFL in military communication networks. Methods The MilQAuth introduces a layered architecture that integrates lightweight authentication, privacy‐preserving encryption, and resilience against both classical and quantum adversaries. Formal validation is provided through the Real‐or‐Random (RoR) model and BAN logic, while automated security verification is conducted using Scyther. Results A comparative performance evaluation demonstrates that MilQAuth achieves reduced computational and communication costs while ensuring stronger privacy guarantees than existing QFL protocols. Conclusions The results confirm that the proposed solution, MilQAuth, not only resists known quantum‐era attacks but also satisfies the stringent security requirements of military‐grade communication systems. Beyond defense applications, the framework remains adaptable to other critical infrastructures, including healthcare and industrial IoT, where secure and privacy‐preserving federated learning is essential.
ABSTRACT Background Federated learning (FL) has emerged as a promising paradigm for distributed model training without centralizing raw data, yet it remains vulnerable to gradient leakage, inference attacks, and malicious updates. The integration of quantum technologies into FL, as proposed by Li et al. through two protocols for quantum federated learning (QFL), aims to mitigate these risks. However, a detailed cryptanalysis of their secure inner‐product estimation and incremental learning protocols reveals critical weaknesses, including susceptibility to replay attacks, quantum tomography, entanglement manipulation, and gradient inversion. These vulnerabilities become particularly severe in mission‐critical environments such as military communication networks, where compromised information may directly expose strategic operations and intelligence. Objective To address these limitations, we propose MilQAuth, an improved quantum‐resistant authentication framework for QFL in military communication networks. Methods The MilQAuth introduces a layered architecture that integrates lightweight authentication, privacy‐preserving encryption, and resilience against both classical and quantum adversaries. Formal validation is provided through the Real‐or‐Random (RoR) model and BAN logic, while automated security verification is conducted using Scyther. Results A comparative performance evaluation demonstrates that MilQAuth achieves reduced computational and communication costs while ensuring stronger privacy guarantees than existing QFL protocols. Conclusions The results confirm that the proposed solution, MilQAuth, not only resists known quantum‐era attacks but also satisfies the stringent security requirements of military‐grade communication systems. Beyond defense applications, the framework remains adaptable to other critical infrastructures, including healthcare and industrial IoT, where secure and privacy‐preserving federated learning is essential.
ABSTRACT Objective To design a secure Federated Learning (FL) framework for Internet of Medical Things (IoMT) that protects sensitive patient data from both classical and quantum attacks. Methods Proposed the QSFedMA‐IoMT protocol integrating quantum and classical security techniques. Utilized entanglement‐based E91 protocol for generating a highly secure root key to establish trust. Applied BB84 protocol for efficient generation of per‐round session keys during FL updates. Incorporated classical cryptographic scheme AES‐GCM for secure communication. Employed privacy‐enhancing techniques such as norm‐clipping and Gaussian noise to mitigate information leakage during model training. Results Our work demonstrates robust resistance against both classical and quantum adversaries, while enhancing data privacy through secure key distribution and differential privacy mechanisms. It ensures the integrity of model updates within the federated learning process and achieves an effective balance between strong security guarantees and computational efficiency, making it well‐suited for IoMT environments. Conclusion The QSFedMA‐IoMT protocol delivers a robust and practical hybrid framework for securing federated learning in healthcare systems. By integrating E91 and BB84 protocols, it strengthens key management and trust establishment. The combination of quantum security with classical privacy‐preserving techniques ensures resilience, scalability, and efficiency. Overall, this work provides a promising direction for secure and privacy‐aware federated learning in next‐generation IoMT applications.
AI-driven medical diagnostics increasingly requires collaborative model training across institutions, yet centralizing patient data conflicts with privacy regulations. Federated Learning enables distributed training without raw data sharing, but remains vulnerable to gradient inversion and model leakage attacks. Furthermore, harvest-now-decrypt-later attacks render computationally secure protocols insufficient for protecting long-lived medical records. Quantum communication offers information-theoretic security immune to such threats, making Quantum Federated Learning (QFL) a compelling framework for healthcare. However, practical deployment is constrained by communication overhead and quantum channel noise. We present a systematic quantitative study of communication, convergence, and noise trade-offs in QFL, introducing two complementary strategies to reduce quantum transmissions: (1) structured parameter reduction via light-cone feature selection in parameterized quantum circuits, and (2) a Hybrid QFL architecture that dynamically switches between centralized and decentralized aggregation. We show that Hybrid QFL reduces total quantum transmissions from 3TNMP, the cost of pure Centralized QFL, to {3t+2(T−t)}NMP over T rounds while preserving near-centralized convergence. We further demonstrate that decentralized aggregation is more noise-resilient under depolarizing noise, and evaluate Steane code-based quantum error correction in high-noise regimes. Our results provide an integrated design framework for communication-efficient, noise-aware QFL, clarifying practical trade-offs for scalable quantum-secure distributed learning in healthcare.
The rapid growth of the Internet of Things (IoT) is transforming industries such as healthcare, transportation, and smart infrastructure, but it also amplifies critical concerns regarding data security, privacy, and device authentication. Traditional cryptographic mechanisms offer partial protection, yet they remain vulnerable to emerging quantum computing threats, posing significant risks to IoT systems. Addressing these challenges, this paper introduces FLQC-IoT, a novel security framework that integrates federated learning (FL) with quantum computing (QC) to provide robust, scalable, and future-ready protection. Unlike conventional centralized approaches, FLQC-IoT enables decentralized model training directly on IoT devices, safeguarding sensitive data from exposure. Quantum technologies are incorporated at multiple layers: quantum neural networks accelerate optimization tasks, while quantum key distribution and quantum homomorphic encryption ensure tamper-proof key exchange and secure communication. Furthermore, the framework adapts dynamically to emerging threats through real-time anomaly detection and distributed intelligence, significantly enhancing resilience. Experimental results demonstrate a 15.32 % reduction in computational overhead and a 21.45 % decrease in authentication rounds compared to state-of-the-art methods, highlighting the framework’s efficiency and practical readiness. These findings underscore the significance and transformative impact of FLQC-IoT, providing a scalable, privacy-preserving, and resilient approach to securing IoT networks across critical domains, including healthcare, university campus networks, smart infrastructure, and transportation.
ABSTRACT Objective With the rapid advancement of quantum computing technologies, Quantum Federated Learning (QFL) has emerged as a promising framework for privacy‐preserving quantum machine learning (QML). QFL provides a promising approach for privacy‐preserving quantum machine learning by enabling collaborative model training without sharing raw data. However, malicious or unreliable quantum clients may submit poisoned or low‐quality models, threatening the reliability of global aggregation. This study aims to develop a secure and robust QFL framework against such adversarial behaviors. Methods We propose a novel Quantum Federated Lasso (QFLasso) framework built on a quantum blockchain infrastructure. Local quantum models are trained with Lasso regularization to achieve sparse representations, and model updates are recorded on the blockchain. Quantum validators verify the quality and integrity of submitted models before aggregation. An anonymous quantum consensus mechanism is further introduced to ensure reliable agreement while preserving validator anonymity. Result The proposed framework integrates quantum Lasso optimization, federated learning, blockchain‐based verification, and anonymous quantum consensus to enhance the security, robustness, and privacy preservation of distributed quantum learning systems. The validator verification mechanism effectively filters unreliable model updates, while the anonymous consensus protocol improves resistance against dishonest or colluding validators and prevents targeted attacks on individual participants. Conclusion QFLasso provides a secure and scalable architecture for privacy‐preserving quantum intelligence, offering potential applications in secure quantum finance, distributed quantum sensing, and collaborative quantum medical diagnosis.
Recent advancements in Quantum Neural Networks (QNNs) have demonstrated theoretical and experimental performance superior to their classical counterparts in a wide range of applications. However, existing centralized QNNs cannot solve many real-world problems because collecting large amounts of training data to a common public site is time-consuming and, more importantly, violates data privacy. Federated Learning (FL) is an emerging distributed machine learning framework that allows collaborative model training on decentralized data residing on multiple devices without breaching data privacy. Some initial attempts at Quantum Federated Learning (QFL) either only focus on improving the QFL performance or rely on a trusted quantum server that fails to preserve data privacy. In this work, we propose CryptoQFL, a QFL framework that allows distributed QNN training on encrypted data. CryptoQFL is (1) secure, because it allows each edge to train a QNN with local private data, and encrypt its updates using quantum homomorphic encryption before sending them to the central quantum server; (2) communication-efficient, as CryptoQFL quantize local gradient updates to ternary values, and only communicate non-zero values to the server for aggregation; and (3) computation-efficient, as CryptoQFL presents an efficient quantum aggregation circuit with significantly reduced latency compared to state-of-the-art approaches.
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.