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.
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.
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.
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.
The amalgamation of quantum computing and artificial intelligence (AI) is poised to redefine the capabilities of future 6G networks. This paper presents a structured, system-level analysis of Quantum-enabled AI (QAI) for 6G, organized around a unifying technical framework that progresses from foundations to algorithms, integration, security and deployment. Specifically, we ask: How can quantum technologies be systematically integrated into AI-native 6G networks in a secure, scalable and practically deployable manner? To answer this, we first establish foundational concepts in quantum computing and hybrid quantum-classical architectures as the enabling substrate. Next, we classify quantum machine learning (QML) algorithms by learning paradigm, quantum subroutine, hardware model and ML pipeline stage, directly mapping each category to specific 6G network functions. Building on this algorithmic layer, we examine how QAI integrates into 6G AI pipelines across training, inference, sensing and federated learning. Beyond taxonomy, we provide a comparative tradeoff analysis of representative QML methods, highlighting their suitability, latency implications, hardware readiness and limitations across practical 6G communication scenarios. We then analyze the security implications of this integration, addressing new attack vectors, adversarial threats, hardware vulnerabilities and post-quantum cryptographic requirements that arise specifically at the quantum-classical interface. Finally, we identify the architectural, algorithmic and regulatory barriers that must be addressed before deployment and synthesize lessons learned and strategic recommendations for researchers, developers and policymakers. This layered, interdependent structure, from hardware substrate through algorithmic capability to secure deployment, provides a coherent roadmap for advancing trustworthy QAI ecosystems in 6G.
In recent years, the growing demand for secure, efficient, and scalable machine learning has highlighted significant challenges in traditional Federated Learning (FL) systems. These challenges include computational inefficiencies in handling large-scale, high-dimensional data, privacy concerns, and the limitations of classical hardware in solving complex optimization problems. Additionally, the increasing complexity of data and the need for more robust privacy-preserving methods have pushed the boundaries of classical FL. To address these issues, this paper presents a detailed framework for Quantum Federated Learning (QFL), where quantum computing and FL converge to enable distributed quantum model training with enhanced privacy preservation. The system is modeled using local quantum models on clients and a global aggregation strategy at the server. We develop quantum gradient-based optimization with quantum cross-entropy loss, privacy preservation through encryption, and robustness to quantum noise. A set of theorems is provided to prove the correctness and efficiency of the model, supported by rigorous mathematical formulations. We used IBM Qiskit for the simulation and compared our proposed QFL with classical FL. The results demonstrate that our QFL outperformed classical FL in terms of accuracy, precision, recall, and F1 score.
Federated learning (FL) has emerged as a compelling paradigm for big-data analytics, enabling collaborative training of a global model across distributed mobile or edge devices without centralizing raw data. Yet conventional FL is challenged by model drift under statistical heterogeneity and by the limited computational budgets of client devices, which together hinder convergence and scalability-particularly when the i.i.d. assumption is violated. To address these limitations, we propose a personalized quantum federated learning (QFL) framework that combines quantum machine learning with distributed optimization to deliver client-specific adaptation atop a compact shared representation. Empirically, our method outperforms state-of-the-art baselines on non-IID workloads while reducing computational complexity through a smaller set of trainable parameters.
The United Nations Sustainable Development Goals (SDGs) demand intelligent, inclusive, and ethically driven solutions. Artificial Intelligence (AI) is a disruptive technology in this space, but its application must be efficient and privacy-preserving. This review explores the integration of Quantum Computing (QC) with Federated Learning (FL) into a novel architecture: Quantum-Federated Learning (QFL). QFL facilitates decentralized, secure model training across clients like hospitals or smart grids without centralizing sensitive data. While FL alone faces computational limitations, QC addresses these through parallelism and high-speed optimization. The proposed QFL framework enables edge nodes to train quantum-enhanced models locally and share only encrypted updates with a central quantum aggregation server. We detail the QFL architecture—comprising quantum-enabled clients, a secure communication layer leveraging quantum cryptography, and a quantum server—and its workflow. The review critically analyzes how QFL can develop applications supporting specific SDGs: SDG 3 (privacy-preserving collaborative healthcare diagnostics), SDG 7 (optimized demand forecasting in decentralized smart grids), SDG 9 (secure predictive maintenance in industry), and SDG 13 (collaborative climate modeling without infringing data sovereignty). Major challenges such as hardware limitations, standardization, and quantum-security issues are discussed. The paper concludes that QFL represents a strategic milestone towards creating AI systems that are not only intelligent and high-performing but also ethical, reliable, and sustainable.