The proliferation of large-scale analytics across distributed edge–cloud environments has raised critical challenges related to data privacy, communication overhead, and computational efficiency. To address these issues, this paper proposes a Quantum-Guided Federated Transformer (QGFT) framework for privacy-preserving large-scale analytics, integrating quantum-inspired optimization with federated transformer learning. In the proposed model, a quantum-guided variational optimizer leverages superposition-driven exploration and entanglement-inspired correlation modeling to adaptively optimize transformer attention weights during decentralized training, without sharing raw data. Federated aggregation is enhanced using privacy-aware adaptive parameter fusion, enabling robust convergence under heterogeneous and non-IID client data distributions. Extensive evaluations conducted on large-scale benchmark datasets demonstrate that QGFT achieves a 3.8–6.4% improvement in analytics accuracy, a 28–35% reduction in communication overhead, and a 22–31% faster convergence rate compared to conventional federated transformer and Fed Avg-based approaches. Furthermore, the integration of quantum-guided optimization improves model generalization by 18.6% under high data heterogeneity. Differential privacy analysis confirms that the proposed framework maintains a privacy leakage probability below 10⁻⁶, satisfying stringent regulatory requirements. These results indicate that QGFT provides a scalable, secure, and efficient solution for next-generation privacy-preserving analytics in federated edge–cloud intelligence. The framework enhances the confidentiality of data during the collaborating process of different nodes while preserving the accuracy of the models across multiple resources. In addition, QGFT is adaptable to all types of edge devices, thus enabling intelligent distributed systems to use Privacy-Aware systems.
The proliferation of large-scale analytics across distributed edge–cloud environments has raised critical challenges related to data privacy, communication overhead, and computational efficiency. To address these issues, this paper proposes a Quantum-Guided Federated Transformer (QGFT) framework for privacy-preserving large-scale analytics, integrating quantum-inspired optimization with federated transformer learning. In the proposed model, a quantum-guided variational optimizer leverages superposition-driven exploration and entanglement-inspired correlation modeling to adaptively optimize transformer attention weights during decentralized training, without sharing raw data. Federated aggregation is enhanced using privacy-aware adaptive parameter fusion, enabling robust convergence under heterogeneous and non-IID client data distributions. Extensive evaluations conducted on large-scale benchmark datasets demonstrate that QGFT achieves a 3.8–6.4% improvement in analytics accuracy, a 28–35% reduction in communication overhead, and a 22–31% faster convergence rate compared to conventional federated transformer and Fed Avg-based approaches. Furthermore, the integration of quantum-guided optimization improves model generalization by 18.6% under high data heterogeneity. Differential privacy analysis confirms that the proposed framework maintains a privacy leakage probability below 10⁻⁶, satisfying stringent regulatory requirements. These results indicate that QGFT provides a scalable, secure, and efficient solution for next-generation privacy-preserving analytics in federated edge–cloud intelligence. The framework enhances the confidentiality of data during the collaborating process of different nodes while preserving the accuracy of the models across multiple resources. In addition, QGFT is adaptable to all types of edge devices, thus enabling intelligent distributed systems to use Privacy-Aware systems.
As real-time data processing becomes central to industries like retail, e-commerce, and entertainment, ensuring both computational efficiency and data privacy is increasingly vital. This study proposes a novel hybrid framework that integrates quantum-enhanced transformers within a Federated Learning (FL) system to perform real-time consumer sentiment analysis. While FL ensures data privacy by decentralizing model training across edge devices, it struggles with issues like high communication overhead and slow convergence in non-IID environments. On the other hand, quantum-enhanced models offer computational acceleration but often lack sufficient privacy safeguards, especially during quantum circuit operations. This research addresses these challenges by developing a quantumenhanced transformer optimized for sentiment analysis and embedding it into a privacy-preserving FL architecture. Advanced encryption techniques such as differential privacy and quantum homomorphic encryption are incorporated to mitigate data leakage risks. The study adopts a four-phase methodology: model development using quantum simulation platforms (e.g., IBM Qiskit), FL integration using FedAvg protocols, system validation through benchmarking against classical models, and real-time deployment simulations across industry-specific datasets. Results show that the proposed system achieves superior performance in latency, model accuracy, and privacy metrics compared to traditional FL models. Despite challenges related to quantum noise and heterogeneity in client data, the system demonstrates robustness and scalability under real-time conditions. This research contributes to the field by providing a viable, secure, and scalable solution for deploying quantum-AI in decentralized environments. It opens pathways for future research in hybrid quantum-classical architectures and domain-specific AI applications that require enhanced privacy and speed.
Abstract This study presents a Federated Quantum Variational Quantum Classifier (FedVQC) framework designed to address critical challenges in healthcare data privacy and computational efficiency, particularly in the domain of genomic analysis. Conventional machine learning approaches in healthcare often face obstacles stemming from privacy concerns, regulatory constraints, and the fragmented nature of medical data across different institutions. The proposed FedVQC framework leverages the principles of federated learning and quantum machine learning to enable secure, decentralized training of predictive models without requiring the sharing of sensitive patient data. Extensive experimentation on multiple genomic datasets reveals a strong correlation between the number of participating institutions and the model's accuracy, with performance consistently improving as more clients contribute to the training process. The framework demonstrates high accuracy levels, ranging from 70% to 96%, across diverse datasets, highlighting its robustness and applicability in healthcare analytics. By harnessing the computational power of quantum computing and the collaborative nature of federated learning, the FedVQC framework provides a scalable and secure solution for genomic research. This study underscores the potential of quantum federated learning in advancing privacy-preserving precision medicine, large-scale genomic collaboration, and broader applications in bioinformatics, drug discovery, and personalized healthcare analytics
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.
The edge computing environment faces a challenge in maintaining data privacy while performing machine learning tasks. The current solutions for privacy preservation are inefficient or do not provide security guarantees, causing a critical gap in the edge computing environment, which is resource-constrained. The aim of this research work is to design a hybrid quantum-classical computing system that addresses the challenges of privacy preservation and efficiency in machine learning tasks. We design a novel architecture combining variational quantum circuits for secure data encoding with classical neural networks for scalable inference, implemented through federated learning protocols enhanced with quantum differential privacy mechanisms. Experimental evaluation demonstrates a 35% reduction in communication overhead compared to traditional federated learning approaches while maintaining 99.2% classification accuracy across diverse datasets. The quantum-enhanced privacy mechanisms achieve provable security guarantees with information-theoretic bounds, outperforming conventional cryptographic methods by 28% in computational overhead reduction. This hybrid approach establishes a new paradigm for secure and efficient machine learning deployment on edge devices, enabling privacy-preserving artificial intelligence in healthcare, finance, and IoT applications.
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.
Federated Learning (FL) has become increasingly popular across different sectors, offering a way for clients to work together to train a global model without sharing sensitive data. It involves multiple rounds of communication between the global model and participating clients, which introduces several challenges like high communication costs, heterogeneous client data, prolonged processing times, and increased vulnerability to privacy threats. In recent years, the convergence of federated learning and parameterized quantum circuits has sparked significant research interest, with promising implications for fields such as healthcare and finance. By enabling decentralized training of quantum models, it allows clients or institutions to collaboratively enhance model performance and outcomes while preserving data privacy. Recognizing that Fisher information can quantify the amount of information that a quantum state carries under parameter changes, thereby providing insight into its geometric and statistical properties. We intend to leverage this property to address the aforementioned challenges. In this work, we propose a Quantum Federated Learning (QFL) algorithm that makes use of the Fisher information computed on local client models, with data distributed across heterogeneous partitions. This approach identifies the critical parameters that significantly influence the quantum model's performance, ensuring they are preserved during the aggregation process. Our research assessed the effectiveness and feasibility of QFL by comparing its performance against other variants, and exploring the benefits of incorporating Fisher information in QFL settings. Experimental results on ADNI and MNIST datasets demonstrate the effectiveness of our approach in achieving better performance and robustness against the quantum federated averaging method.