Object tracking with radar has become a key part of many IoT applications, such as smart transportation, autonomous robotics, and ambient surveillance. Nevertheless, conventional machine learning techniques have fatal problems, such as privacy, noisy radar signals, latency, and generalization in the distributed IoT systems. This paper proposes a new model, Quantum-Assisted Federated Learning (QAFL), a combination of quantum machine learning (QML) and federated learning (FL) to solve these problems, which can effectively and privately identify radar objects and their routes. The suggested QAFL architecture has a new Hybrid Angle-Amplitude Encoding (HAAE) scheme with multi-layer Variational Quantum Circuits (VQCs) to support the effective extraction of features in the presence of noisy and non-homogeneous radar sensor data. We also present a Quantum-Enhanced Federated Averaging (Quantum-FedAvg) algorithm that can be used to improve the efficiency of the training, privacy, and scalability of distributed IoT nodes. Comprehensive experimental tests based on the CARRADA automotive radar dataset show that QAFL achieves large performance improvements compared to classical federated learning baselines, in terms of classification accuracy improvements of up to 7.2 percentage points with the low signal-to-noise ratio (SNR) regime, trajectory prediction error reduction of up to 23 percent, as well as considerable communication overhead and training latency reductions. Such results highlight the enormous potential of quantum-enhanced federated learning systems to radar-based internet-of-things tracking systems.
Object tracking with radar has become a key part of many IoT applications, such as smart transportation, autonomous robotics, and ambient surveillance. Nevertheless, conventional machine learning techniques have fatal problems, such as privacy, noisy radar signals, latency, and generalization in the distributed IoT systems. This paper proposes a new model, Quantum-Assisted Federated Learning (QAFL), a combination of quantum machine learning (QML) and federated learning (FL) to solve these problems, which can effectively and privately identify radar objects and their routes. The suggested QAFL architecture has a new Hybrid Angle-Amplitude Encoding (HAAE) scheme with multi-layer Variational Quantum Circuits (VQCs) to support the effective extraction of features in the presence of noisy and non-homogeneous radar sensor data. We also present a Quantum-Enhanced Federated Averaging (Quantum-FedAvg) algorithm that can be used to improve the efficiency of the training, privacy, and scalability of distributed IoT nodes. Comprehensive experimental tests based on the CARRADA automotive radar dataset show that QAFL achieves large performance improvements compared to classical federated learning baselines, in terms of classification accuracy improvements of up to 7.2 percentage points with the low signal-to-noise ratio (SNR) regime, trajectory prediction error reduction of up to 23 percent, as well as considerable communication overhead and training latency reductions. Such results highlight the enormous potential of quantum-enhanced federated learning systems to radar-based internet-of-things tracking systems.
Federated learning (FL) is a privacy-preserving alternative to centralized machine learning, where model training is performed on local devices and only global model updates are shared, effectively addressing challenges, such as data silos and privacy protection. Recently, quantum FL (QFL), an emerging FL branch, has garnered significant attention in many industry applications. However, existing QFL algorithms predominantly employ average weighting for global model training, which shows poor performance on heterogeneous healthcare data. To address this challenge, this study proposes a dynamic aggregation QFL algorithm (DAQFL) for intelligent diagnosis. Specifically, it utilizes quantum neural networks (QNNs) as local training models and designs corresponding variational quantum circuits (VQC). To mitigate performance degradation caused by the heterogeneity of medical industrial data, a dynamic aggregation method based on accuracy is proposed to enhance global model performance effectively. Extensive experiments with three distribution settings, including independent and identically distributed (IID), non-independent and identically distributed (Non-IID), and long-tail datasets, show that DAQFL outperforms baseline algorithms in accuracy and training speed. It also performs well in privacy protection and robustness of anti-noise, improving its suitability for real-world medical applications.
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.
Advanced Driver Assistance Systems (ADAS) increasingly employ Federated Learning (FL) to collaboratively train models across distributed vehicular nodes while preserving data privacy. Yet, conventional FL aggregation remains susceptible to noise, latency, and security constraints inherent to real-time vehicular networks. This paper introduces Noise-Resilient Quantum Federated Learning (NR-QFL), a hybrid quantum-classical framework that enables secure, low-latency aggregation through variational quantum circuits (VQCs) operating under Noisy Intermediate-Scale Quantum (NISQ) conditions. The framework encodes model parameters as quantum states with adaptive gate reparameterization, ensuring bounded-error convergence and provable resilience under Completely Positive Trace-Preserving (CPTP) dynamics. NR-QFL employs quantum entropy-based client selection and multi-server coordination for fairness and stability. Empirical validation shows consistent convergence with reduced gradient variance, lower communication overhead, and enhanced noise tolerance under constrained edge conditions. The framework establishes a scalable foundation for quantum-enhanced federated learning, enabling secure, efficient, and dynamically stable ADAS intelligence at the vehicular edge.
The blistering development of the Internet of Things (IoT) has brought an urgent necessity to an efficient data processing at the end of the network, where devices are characterized by few computational capabilities, and little to no energy availability. Conventional machine learning (ML) solutions can offer high-performance on a centralized system, but do not provide the edge environments with the required latency and the corresponding performance. This paper gives a quantum machine learning (QML)-backed architecture that is used to optimize IoT edge processing, inspiring quantum feature encoding along with quantum neural networks with classical post-processing resource allocation. Different simulated IoT datasets were tested on the framework and they all represent applications to smart home, industrial IoT, and environment monitoring. According to experimental findings, processing latency (improved by 39 percent), energy consumption (improved by 40 percent), memory reorganization (improved by 40 percent), and prediction accuracy (improved by 6 per cent.) were significantly less than conventional ML models. The results demonstrate that QML models are able to make real-time decisions significant and scalable as well as sustainable in edge computing. Quantum-enhanced edge intelligence can significantly improve IoT data processing speed, efficiency, and smart decision-making.
Advanced Driver Assistance Systems (ADAS) increasingly employ Federated Learning (FL) to collaboratively train models across distributed vehicular nodes while preserving data privacy. Yet, conventional FL aggregation remains susceptible to noise, latency, and security constraints inherent to real-time vehicular networks. This paper introduces Noise-Resilient Quantum Federated Learning (NR-QFL), a hybrid quantum-classical framework that enables secure, low-latency aggregation through variational quantum circuits (VQCs) operating under Noisy Intermediate-Scale Quantum (NISQ) conditions. The framework encodes model parameters as quantum states with adaptive gate reparameterization, ensuring bounded-error convergence and provable resilience under Completely Positive Trace-Preserving (CPTP) dynamics. NR-QFL employs quantum entropy-based client selection and multi-server coordination for fairness and stability. Empirical validation shows consistent convergence with reduced gradient variance, lower communication overhead, and enhanced noise tolerance under constrained edge conditions. The framework establishes a scalable foundation for quantum-enhanced federated learning, enabling secure, efficient, and dynamically stable ADAS intelligence at the vehicular edge
The rapid growth of industrial Internet of Things (IIoT) systems has created new challenges for anomaly detection in high-dimensional, multivariate time-series, where privacy, scalability, and communication efficiency are critical. Classical federated learning approaches mitigate privacy concerns by enabling decentralized training, but they often struggle with highly non-linear decision boundaries and imbalanced anomaly distributions. To address this gap, we propose a Federated Quantum Kernel Learning (FQKL) framework that integrates quantum feature maps with federated aggregation to enable distributed, privacy-preserving anomaly detection across heterogeneous IoT networks. In our design, quantum edge nodes locally compute compressed kernel statistics using parameterized quantum circuits and share only these summaries with a central server, which constructs a global Gram matrix and trains a decision function (e.g., Fed-QSVM). Experimental results on synthetic IIoT benchmarks demonstrate that FQKL achieves superior generalization in capturing complex temporal correlations compared to classical federated baselines, while significantly reducing communication overhead. This work highlights the promise of quantum kernels in federated settings, advancing the path toward scalable, robust, and quantum-enhanced intelligence for next-generation IoT infrastructures.