As a key paradigm of future 6G networks, Space-Air-Ground Integrated Networks (SAGIN) has been envisioned to provide numerous intelligent applications that necessitate the cooperation of a multitude of terrestrial devices for machine learning (ML) model training. Utilizing the satellite as the central server, federated learning (FL) offers a promising strategy for distributed training with enhanced data security and privacy. However, employing FL in SAGIN faces challenges in managing vast datasets, training complicated ML models, and ensuring security in long-distance transmission of ML models. In this article, we propose a quantum-empowered FL framework integrating variational quantum algorithms (VQA) and quantum relays in SAGIN. Our approach employs VQA-based ML for local training of FL, addressing the complexity emerging from vast datasets and ML models. Furthermore, supported by unmanned aerial vehicles (UAVs) and high-altitude platform stations (HAPS), the proposed quantum relay scheme via quantum teleportation guarantees security in long-distance model transmission. We present a case study to validate the proposed VQA-based local training and quantum relaying model transmission. The numerical results demonstrate the feasibility and efficiency of the proposed VQA-based FL framework and the quantum relay-based model transmission. Our approach highlights the potential of integrating quantum techniques with FL in SAGIN, which can enable secure, efficient, and advanced edge intelligence applications.
As a key paradigm of future 6G networks, Space-Air-Ground Integrated Networks (SAGIN) has been envisioned to provide numerous intelligent applications that necessitate the cooperation of a multitude of terrestrial devices for machine learning (ML) model training. Utilizing the satellite as the central server, federated learning (FL) offers a promising strategy for distributed training with enhanced data security and privacy. However, employing FL in SAGIN faces challenges in managing vast datasets, training complicated ML models, and ensuring security in long-distance transmission of ML models. In this article, we propose a quantum-empowered FL framework integrating variational quantum algorithms (VQA) and quantum relays in SAGIN. Our approach employs VQA-based ML for local training of FL, addressing the complexity emerging from vast datasets and ML models. Furthermore, supported by unmanned aerial vehicles (UAVs) and high-altitude platform stations (HAPS), the proposed quantum relay scheme via quantum teleportation guarantees security in long-distance model transmission. We present a case study to validate the proposed VQA-based local training and quantum relaying model transmission. The numerical results demonstrate the feasibility and efficiency of the proposed VQA-based FL framework and the quantum relay-based model transmission. Our approach highlights the potential of integrating quantum techniques with FL in SAGIN, which can enable secure, efficient, and advanced edge intelligence applications.
Space-air-ground integrated networks (SAGINs) are emerging as a fundamental architecture for 6G systems to enable massive connectivity, novel applications, extreme data rates, ultra-low latency, and multi-dimensional networking. The complex nature of SAGINs has driven researchers to explore classical machine learning (CML) as a powerful tool for modeling and optimizing these multi-layer networks. Although ML offers many benefits, it will suffer from exponential complexity for SAGIN design mainly due to high-dimensional data and problem spaces. Therefore, the NP-hard problems are challenging for CML to enable SAGINs efficiently. To address this critical challenge, we present a comprehensive survey examining the integration of quantum ML (QML) with SAGINs. We systematically evaluate quantum-enhanced SAGIN technologies through detailed performance metrics and create a new classification system for hybrid network architectures. We thoroughly examine key integration strategies and analyze major technical challenges, including how quantum states degrade in space, how to optimally distribute resources, and how to develop security protocols. We show how QML can solve the growing complexity of 6G networks by improving network capacity, intelligence, and security. The research also creates a comprehensive framework for quantum-enhanced SAGINs that researchers and industry professionals can use as a guide when developing next-generation communication systems. Our findings examine the transformative potential of QML in reshaping global communications infrastructure, which presents a structured approach to navigating the intricate challenges of advanced networking technologies and also provides a critical pathway for addressing the exponentially growing computational demands of future communication ecosystems.
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.
With rapid developments of artificial intelligence (AI) and wireless communication technologies, autonomous vehicular networks (AVNs) have emerged to provide convenient, comfortable, and safe driving services. Especially, due to the provable and unconditional security, quantum communication technology is increasingly being applied to promote the security of data delivery in AVNs, which forms the promising quantum AVNs (QAVNs). Federated learning (FL) is essential in QAVNs to implement intelligent and autonomous driving, where extensive distributed data is utilized to cooperatively train AI models, preserve user privacy, and reduce resource consumption. However, the FL unavoidably faces several possible privacy and security problems as a result of the intricate interconnections between several autonomous vehicles or infrastructures. In this article, we investigate the security and privacy of FL in QAVNs. Specifically, we first establish the system architecture, including quantum key distribution protocol, space-air-ground integrated network (SAGIN)-enabled QAVNs model, and FL framework in QAVNs. Furthermore, we describe the privacy and security threats to FL in QAVNs, i.e., inference attack, eavesdropping attack, and sybil attack. Following that, we give a case study for designing a secure FL scheme using the local differential privacy mechanism and the homomorphic encryption mechanism. Finally, we suggest several potential future research directions in QAVNs.
In the upcoming 6G era, existing ground networks have evolved toward space-air-ground integrated networks (SAGIN), providing ultra-high data rates, seamless network coverage, and ubiquitous intelligence for communications of applications and services. However, conventional communications in SAGIN still face data confidentiality issues. Fortunately, the concept of Quantum Key Distribution (QKD) over SAGIN is able to provide information-theoretic security for secure communications in SAGIN with quantum cryptography. Therefore, in this article, we propose quantum-secured SAGIN (Q-SAGIN) which are feasible to achieve proven secure communications using quantum mechanics to protect data channels between space, aerial, and ground nodes. Moreover, we propose a universal QKD service provisioning framework to minimize the cost of QKD services under the uncertainty and dynamics of communications in Q-SAGIN. In this framework, fiber-based QKD services are deployed in passive optical networks with the advantages of low loss and high stability. Moreover, the widely covered and flexible satellite-and UAV-based QKD services are provisioned as a supplement during the real-time data transmission phase. Finally, to examine the effectiveness of the proposed concept and framework, a case study of Q-SAGIN in the Metaverse is conducted where uncertain and dynamic factors of the secure communications in Metaverse applications are effectively resolved in the proposed framework.
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.
The Internet of Things (IoT) has revolutionized various sectors by enabling seamless device interaction. However, the proliferation of IoT devices has also raised significant security and privacy concerns. Traditional security measures often fail to address these concerns due to the unique characteristics of IoT networks, such as heterogeneity, scalability, and resource constraints. This survey paper adopts a thematic exploration approach for a comprehensive analysis to investigate the convergence of quantum computing, federated learning, and 6G wireless networks. This novel intersection is explored to significantly improve security and privacy within the IoT ecosystem. To enable several secure, intelligent IoT applications, quantum computing, with its superior computational capabilities, can strengthen encryption algorithms, making IoT data more secure. Federated learning, a decentralized machine learning approach, allows IoT devices to learn a shared model while keeping all the training data on the original device, thereby enhancing privacy. This synergy becomes even more crucial when integrated with the high-speed, low-latency capabilities of 6G networks, which can facilitate real-time, secure data processing and communication among many IoT devices. Second, we discuss the latest developments, offering an up-to-date overview of advanced solutions, available datasets, and key performance metrics and summarizing the vital insights, challenges, and trends in securing IoT systems. Third, we design a conceptual framework for integrating quantum computing in federated learning, adapted for 6G networks. Finally, we highlight the future advancements in quantum technologies and 6G networks and summarize the implications for IoT security, paving the way for researchers and practitioners in the field of IoT security.