In the rapidly evolving domain of vehicular metaverse, this study introduces a cutting-edge quantum-based decentralized and heterogeneity-aware federated learning framework for vehicular metaverse named QV-FEDCOM, which stands as a testament to the innovative fusion of quantum computing principles with federated learning (FL). This framework is ingeniously tailored to address the challenges in a vehicular metaverse, offering a cost-efficient and adaptive solution for the dynamic vehicular landscape. QV-FEDCOM is strengthened by key components like quantum sequential-training-program, with reinforcement learning-based dynamic mode switching to reduce communication costs and manage vehicle states adaptively, and the quantum vehicle-context-grouping utilizing hierarchical clustering and simulated annealing for effective vehicle grouping based on contextual data similarity, addressing the complexities of data heterogeneity. Additionally, the integration of quantum-inspired principal component analysis (Q-PCA) enhances memory efficiency, further optimizing the framework. These elements converge in the QV-FEDCOM algorithm, establishing a decentralized, efficient, and context-aware quantum federated learning (QFL) process that redefines learning dynamics in the vehicular metaverse. Our study also introduces an innovative quantum trajectory loss (QTL) function, specifically designed for trajectory prediction tasks, which combines the Huber loss with an angular deviation penalty to robustly handle errors and penalize large deviations in the predicted trajectory angle. The effectiveness of the QV-FEDCOM framework is rigorously validated through comprehensive simulations, with its performance meticulously compared against various adaptations, showcasing its transformative capabilities within the vehicular metaverse ecosystem.
In the rapidly evolving domain of vehicular metaverse, this study introduces a cutting-edge quantum-based decentralized and heterogeneity-aware federated learning framework for vehicular metaverse named QV-FEDCOM, which stands as a testament to the innovative fusion of quantum computing principles with federated learning (FL). This framework is ingeniously tailored to address the challenges in a vehicular metaverse, offering a cost-efficient and adaptive solution for the dynamic vehicular landscape. QV-FEDCOM is strengthened by key components like quantum sequential-training-program, with reinforcement learning-based dynamic mode switching to reduce communication costs and manage vehicle states adaptively, and the quantum vehicle-context-grouping utilizing hierarchical clustering and simulated annealing for effective vehicle grouping based on contextual data similarity, addressing the complexities of data heterogeneity. Additionally, the integration of quantum-inspired principal component analysis (Q-PCA) enhances memory efficiency, further optimizing the framework. These elements converge in the QV-FEDCOM algorithm, establishing a decentralized, efficient, and context-aware quantum federated learning (QFL) process that redefines learning dynamics in the vehicular metaverse. Our study also introduces an innovative quantum trajectory loss (QTL) function, specifically designed for trajectory prediction tasks, which combines the Huber loss with an angular deviation penalty to robustly handle errors and penalize large deviations in the predicted trajectory angle. The effectiveness of the QV-FEDCOM framework is rigorously validated through comprehensive simulations, with its performance meticulously compared against various adaptations, showcasing its transformative capabilities within the vehicular metaverse ecosystem.
In the rapidly evolving domain of vehicular metaverse, this study introduces a cutting-edge quantum-based decentralized and heterogeneity-aware federated learning framework for vehicular metaverse named QV-MetaFL, which stands as a testament to the innovative fusion of quantum computing principles with federated learning (FL). This framework is ingeniously tailored to address the challenges in a vehicular metaverse, offering a cost-efficient and adaptive solution for the dynamic vehicular landscape. QV-MetaFL is strengthened by the quantum sequential-training-program (Q-STP) algorithm, a quantum-based sequential training program that transforms model training, reducing communication costs and adeptly managing vehicle states. Complementing this, the quantum vehicle-context-grouping (Q-VCG) mechanism groups vehicles based on contextual data similarity, effectively tackling the complexities of data heterogeneity. The synergy of Q-STP and Q-VCG culminates in the QV-MetaFL algorithm, a decentralized, efficient, and context-aware quantum federated learning (QFL) process that redefines learning dynamics in the vehicular metaverse. Additionally, our research introduces an innovative composite loss function that amalgamates classical loss metrics with quantum parameter regularization, deftly addressing quantum sensitivity to noise. The effectiveness of the QV-MetaFL framework is rigorously validated through comprehensive simulations, with its performance meticulously compared against various adaptations, showcasing its transformative capabilities within the vehicular metaverse ecosystem.
Quantum federated learning (QFL) is a combination of distributed quantum computing and federated machine learning, integrating the strengths of both to enable privacy-preserving decentralized learning with quantum-enhanced capabilities. It appears as a promising approach for addressing challenges in efficient and secure model training across distributed quantum systems. This paper presents a comprehensive survey on QFL, exploring its key concepts, fundamentals, applications, and emerging challenges in this rapidly developing field. Specifically, we begin with an introduction to the recent advancements of QFL, followed by discussion on its market opportunity and background knowledge. We then discuss the motivation behind the integration of quantum computing and federated learning, highlighting its working principle. Moreover, we review the fundamentals of QFL and its taxonomy. Particularly, we explore federation architecture, networking topology, communication schemes, optimization techniques, and security mechanisms within QFL frameworks. Furthermore, we investigate applications of QFL across several domains which include vehicular networks, healthcare networks, satellite networks, metaverse, and network security. Additionally, we analyze frameworks and platforms related to QFL, delving into its prototype implementations, and provide a detailed case study. Key insights and lessons learned from this review of QFL are also highlighted. We complete the survey by identifying current challenges and outlining potential avenues for future research in this rapidly advancing field.
This work presents vQFL (vehicular Quantum Federated Learning), a new framework that leverages quantum machine learning techniques to tackle key privacy and security issues in autonomous vehicular networks. Furthermore, we propose a server-side adapted fine-tuning method, ft-VQFL,to achieve enhanced and more resilient performance. By integrating quantum federated learning with differential privacy and quantum key distribution (QKD), our quantum vanguard approach creates a multi-layered defense against both classical and quantum threats while preserving model utility. Extensive experimentation with industry-standard datasets (KITTI, Waymo, and nuScenes) demonstrates that vQFL maintains accuracy comparable to standard QFL while significantly improving privacy guaranties and communication security. Our implementation using various quantum models (VQC, QCNN, and SamplerQNN) reveals minimal performance overhead despite the added security measures. This work establishes a crucial foundation for quantum-resistant autonomous vehicle systems that can operate securely in the post-quantum era while efficiently processing the massive data volumes (20-40TB/day per vehicle) generated by modern autonomous fleets. The modular design of the framework allows for seamless integration with existing vehicular networks, positioning vQFL as an essential component for future intelligent transportation infrastructure.
With the rise of autonomous vehicles (AVs), the number of self-driving vehicles has increased in recent years and vehicular computing (VC) was actively used to ensure road safety by controlling AVs. However, the recent escalation in demand for a massive scale vehicle network has prevented VC from achieving its purpose. To overcome this problem, a framework which combined VC and federated learning (FL) was proposed. Although the vehicular FL was initially effective, it also faced challenges as the excessive increase in the amount and size of data generated by AVs and the serious threat of data leakage have severely degraded the performance of vehicular FL. Thus, quantum computing was exploited to further improve on the pre-existing FL and proposed dynamic quantum federated learning (DQFL). This work proposes the application of DQFL to public safety scenarios to investigate the effectiveness of the model in controlling the AVs under real-life constraints.
Quantum Federated Learning (QFL) recently becomes a promising approach with the potential to revolutionize Machine Learning (ML). It merges the established strengths of classical Federated Learning (FL) with the exceptional parallel mechanism and exponential speed enhancements characteristic of quantum computing. While this innovative fusion has garnered considerable attention, a notable gap in current research is the tendency to consider traditional FL and its quantum-enhanced counterpart, QFL, in isolation. This approach often overlooks the critical role of Quantum Machine Learning (QML) in effectively bridging these two domains. Recognizing this, there emerges a pressing need for a comprehensive survey that encompasses the entire spectrum of FL paradigms, from foundational FL concepts to the cutting-edge developments in QFL. Our survey aims to address this need by providing an in-depth exploration of the various facets of FL paradigms, ultimately leading to a thorough understanding of Quantum Federated Learning. We start by emphasizing the driving factors and prevalent research topics related to FL. To develop a more efficient, robust, and precise computing paradigm, we investigate the current solutions that address the concerns of heterogeneity, privacy, security, and evaluation in FL. After that, we explore the possibility of improving the computational efficiency of ML methods by leveraging the computational capabilities of quantum computers. In particular, we discuss the frameworks, evaluation, and applications for QML. Following that, we detail the integration of quantum computing technologies with standard FL, aiming to bolster computational performance and mitigate security and privacy risks. The potential solutions to improve the efficiency (i.e., slimming mechanism) and respect the privacy and security (i.e., quantum key distribution) for QFL are explored. Finally, we outline some critical future directions towards unlocking the full potential of QFL in distributed machine learning.
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.