Federated learning (FL) provides a collaborative model allowing multiple entities to collectively train artificial intelligence (AI) systems while minimizing data sharing to enhance privacy. Despite its advancements, traditional FL still faces security issues during the exchange of model parameters. The advent of quantum networks, which offer inherent security benefits, prompts us to introduce a new paradigm: quantum communication-based FL (QC-FL). This robust, scalable framework improves data control and mitigates security risks associated with parameter exchange. Our study outlines the key technologies and architecture underpinning QC-FL, illustrating its utility in safeguarding sensitive patient data and enhancing intelligent transportation systems.
Federated learning (FL) provides a collaborative model allowing multiple entities to collectively train artificial intelligence (AI) systems while minimizing data sharing to enhance privacy. Despite its advancements, traditional FL still faces security issues during the exchange of model parameters. The advent of quantum networks, which offer inherent security benefits, prompts us to introduce a new paradigm: quantum communication-based FL (QC-FL). This robust, scalable framework improves data control and mitigates security risks associated with parameter exchange. Our study outlines the key technologies and architecture underpinning QC-FL, illustrating its utility in safeguarding sensitive patient data and enhancing intelligent transportation systems.
Ensuring data security and privacy is highly pertinent in today’s era marked by the dominance of collaborative machine learning. This study presents a novel framework, named" Quantum-Resilient Privacy-Preserving Federated Learning for Enhanced Data Security," which leverages quantum computing to strengthen federated learning models against adversarial risks and enhance privacy safeguards. Our research investigates the incorporation of quantum key distribution and entanglement protocols into federated learning, therefore establishing a quantum- resilient methodology. The findings demonstrate the model’s ability to effectively resist advanced threats, hence assuring the maintenance of strong data security measures. The utilization of quantum technology in this context serves to strengthen the fundamental principles of cryptography, while simultaneously introducing a novel approach to enhancing privacy. Moreover, the study explores the potential of the framework to accommodate various datasets from different cooperating entities, while also resolving the difficulties presented by the diversity of the data. The framework’s success in accommodating multiple machine learning models and applications is demonstrated by evaluations of scalability and computing efficiency. In essence, the research paper titled" Quantum-Resilient Privacy-Preserving Federated Learning for Enhanced Data Security" represents a significant progression in the field of safeguarding collaborative machine learning. The quantum-resilient paradigm not only increases privacy but also offers a safe foundation for the future of collaborative machine learning. The issue at hand pertains to the growing apprehensions over the safeguarding of data privacy and security within the context of our technologically advanced day.
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.
Federated learning enables collaborative model training across multiple clients without sharing raw data, thereby enhancing privacy. However, the exchange of model updates can still expose sensitive information. Quantum teleportation, a process that transfers quantum states between distant locations without physical transmission of the particles themselves, has recently been implemented in real-world networks. This position paper explores the potential of integrating quantum teleportation into federated learning frameworks to bolster privacy. By leveraging quantum entanglement and the no-cloning theorem, quantum teleportation ensures that data remains secure during transmission, as any eavesdropping attempt would be detectable. We propose a novel architecture where quantum teleportation facilitates the secure exchange of model parameters and gradients among clients and servers. This integration aims to mitigate risks associated with data leakage and adversarial attacks inherent in classical federated learning setups. We also discuss the practical challenges of implementing such a system, including the current limitations of quantum network infrastructure and the need for hybrid quantum-classical protocols. Our analysis suggests that, despite these challenges, the convergence of quantum communication technologies and federated learning presents a promising avenue for achieving unprecedented levels of privacy in distributed machine learning.
Industrial innovations, driven by technologies such as the Internet of Things (IoT), cloud computing, and Artificial Intelligence (AI), have revolutionized various operational domains, including supply chain management, equipment monitoring, predictive maintenance, and quality control. Deep Learning (DL) has become instrumental in addressing complex, data-driven challenges within industrial settings, relying on large datasets collected from heterogeneous sources. However, centralizing these datasets on cloud platforms poses significant privacy and security risks. Federated Learning (FL) offers a promising solution by enabling distributed model training across multiple nodes while preventing the exchange of raw data. Despite its advantages, FL introduces new vulnerabilities, especially related to the security of communication channels between participating entities. Traditional cryptographic mechanisms, such as digital signatures and model encryption, can mitigate these risks, but the emergence of quantum computing threatens the robustness of conventional solutions. This work explores the integration of Post-Quantum Cryptography (PQC) into FL to enhance security without incurring significant performance degradation. A modular FL architecture with three security levels is proposed: (i) an unprotected baseline; (ii) authenticated communication using ML-DSA digital signatures; and (iii) full protection combining ML-DSA and ML-KEM-512-based key encapsulation. Implemented within the Flower framework, the architecture is evaluated under simulated MitM attacks. Experimental results demonstrate that PQC-enhanced schemes effectively mitigate quantum-resistant threats while maintaining acceptable computational overhead, thereby ensuring model integrity and data confidentiality.
Collaborative threat intelligence via federated learning (FL) faces critical risks from quantum computing, which can compromise classical encryption methods. This study proposes a quantum-secure FL framework using post-quantum cryptography (PQC) to protect cross-organizational data sharing. We expose vulnerabilities in traditional FL through simulated quantum attacks on RSA encrypted gradients and introduce a hybrid architecture integrating NIST-standardized algorithms CRYSTALS-Kyber for key exchange and CRYSTALS-Dilithium for authentication. Testing on APT attack datasets demonstrated 97.6% threat detection accuracy with minimal latency overhead (18.7%), validating real-world viability. A healthcare consortium case study confirmed secure ransomware indicator sharing without breaching privacy regulations. The work highlights the urgency of quantum ready defenses and provides technical guidelines for deploying PQC in FL systems, alongside policy recommendations for standardizing quantum resilience in threat-sharing networks.
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing, addressing privacy concerns. However, traditional FL is vulnerable to attacks targeting model updates, potentially compromising data privacy. This paper proposes a novel decentralized federated learning framework that integrates quantum key distribution (QKD) to establish secure keys for data aggregation. The core claim is that QKD significantly enhances the security and efficiency of FL. The proposed system utilizes QKD to generate and distribute shared keys between participating devices, enabling secure aggregation of model updates while mitigating the risk of individual data exposure. This approach directly addresses the security vulnerabilities inherent in standard FL. The framework's architecture relies on a decentralized network topology, further bolstering resilience against single points of failure. Mathematical formulations detailing key aspects of the system, including key distribution protocols, aggregation processes, and security analysis, are presented. This work represents a significant advancement in secure FL by combining the strengths of two cutting-edge technologies: FL and QKD.