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.
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.
HRMARS - This paper introduces a new Quantum Federated Learning (QFL) system that combines quantum-enhanced transformers and federated learning to make it possible to perform real-time consumer sentiment analysis securely, at scale, and efficiently. The hybrid model proposed overcomes three significant issues: it can be accurate when working with non-IID data, it is communication-efficient, and it can protect data privacy with high security provided by quantum cryptographic protocols. Experimental analyses on IMDB, Sentiment140, Amazon, Yelp, and our own proprietary data showed that QFL is always better in precision, recall, F1 score, and convergence rate than traditional FL-BERT and FL-LSTM models. Further, QFL was able to demonstrate substantial cost and privacy leakage savings in communication at a low cost as well as maintain scalability to one thousand clients. The study fills the gap between concepts in quantum computing and federated learning and provides a practical route to privacy-sensitive distributed intelligence on a practical scale applied to e-commerce and social media analytics.
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.
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.
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.
Quantum Federated Learning (QFL) is an emerging field that harnesses advances in Quantum Computing (QC) to improve the scalability and efficiency of decentralized Federated Learning (FL) models. This paper provides a systematic and comprehensive survey of the emerging problems and solutions when FL meets QC, from research protocol to a novel taxonomy, particularly focusing on both quantum and federated limitations, such as their architectures, Noisy Intermediate Scale Quantum (NISQ) devices, and privacy preservation, so on. With the introduction of two novel metrics, qubit utilization efficiency and quantum model training strategy, we present a thorough analysis of the current status of the QFL research. This work explores key developments and integration strategies, along with the impact of QC on FL, keeping a sharp focus on hybrid quantum-classical approaches. The paper offers an in-depth understanding of how the strengths of QC, such as gradient hiding, state entanglement, quantum key distribution, quantum security, and quantum-enhanced differential privacy, have been integrated into FL to ensure the privacy of participants in an enhanced, fast, and secure framework. Finally, this study proposes potential future directions to address the identified research gaps and challenges, aiming to inspire faster and more secure QFL models for practical use.
The exponential growth of bioinformatics data, including genomic sequences and protein structures, necessitates advanced analytical frameworks that ensure privacy, computational efficiency, and scalability. To address these challenges, we propose an integrated framework that combines Federated Learning (FL) with Quantum Computing (QC) for secure and efficient bioinformatics data analysis across distributed healthcare institutions. The proposed Quantum Federated Learning (QFL) architecture employs variational quantum circuits to expedite local model training, achieving a 35% reduction in computation time compared to classical FL methods. To strengthen security, Quantum Secure Communication Protocols (QSCP) are incorporated, resulting in over 40% enhanced resilience against adversarial attacks relative to conventional encryption schemes. Experimental validation on distributed genomic datasets, such as the Cancer Genome Atlas, demonstrates that QFL attains a classification accuracy of 94.8% in gene expression-based cancer subtype prediction, surpassing traditional FL by 6.3%. Furthermore, the model exhibits a 25% reduction in communication overhead through quantum state compression, ensuring efficient data exchange. This hybrid approach effectively addresses the critical requirements of data privacy, computational acceleration, and model robustness in bioinformatics, establishing a promising pathway for secure and scalable precision medicine solutions empowered by quantum-enhanced federated learning.