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.
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.
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.
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 enables decentralized, privacy-preserving training but remains vulnerable to privacy leakage in the quantum era. Quantum federated learning (QFL) offers a promising path toward enhanced security and efficiency. However, a practical and experimentally validated QFL protocol utilizing near-term quantum techniques to address data privacy has been lacking. Here, we present QuNetQFL, a QFL protocol implemented on quantum networks, in which local model updates are masked with distributed quantum secret keys, offering information-theoretic security during aggregation. We experimentally validate the protocol on a 4-client quantum network and benchmark its performance using the generated keys on quantum and real-world datasets. Adding a single quantum client substantially improves global accuracy for classifying multipartite entangled and nonstabilizer quantum datasets. For language tasks, we apply QuNetQFL to sentiment analysis by federated fine-tuning of a hybrid classical-quantum language model, achieving comparable and robust performance in simulation and on real quantum hardware. Large-scale simulations further demonstrate scalability to 200 clients for handwritten-digit recognition, with rapid convergence and a 75% reduction in communication cost via model compression. Our work establishes a practical and scalable route to quantum-secure federated learning for the emerging quantum internet.
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.
Privacy-preserving approaches are essential in health- care applications where sensitive data is involved. Federated learning (FL) has emerged as a widely adopted approach for collaboratively training decentralized models without sharing individual health records. However, ensuring privacy in health- care data, both during training and when clients exchange their models with a central server, remains a challenge. Bias, fairness, clients heterogeneity, and constrained computation are also challenging factors. To address this challenge, in this paper, a communication-efficient and privacy-preserving hybrid Federated learning (HFL) framework is specifically designed for mental healthcare applications. Two HFL approaches, namely Clustered Federated Learning (CFL) and Quantum Federated Learning (QFL), have been proposed. CFL focuses on leveraging the learning behaviour of clients and Conversely, QFL introduces a new phase to FL by incorporating a variational quantum classifier (VQC) for classification tasks. Angle encoding is used for a quantum state preparation to enhance data encoding and learning the quantum model. Experiments were conducted on independent and identically distributed (iid) and non-independent and identically distributed (non-iid) data to evaluate the performance of the proposed methods with state-of-the-art results available in the literature. The results demonstrate exceptional performance in the case of QFL, achieving an accuracy of 84.00%. CFL also exhibits promising results with an accuracy of 78.396%. Additionally, QFL achieves 18.75% better recall and CFL has 6.24% better precision than traditional FL. Nevertheless, it’s crucial to remember that every model has advantages and disadvantages of its own.
The mix of quantum computing and machine learning has introduced strong quantum models able to resolve challenging tasks efficiently. Still, the use of centralized QML systems could present challenges with privacy because sharing quantum data with untrusted aggregators is especially risky. This study describes a method called Privacy-Preserving Quantum Federated Learning (PQFL), which allows multiple clients to jointly train quantum models while maintaining data confidentiality. This system offers security by combining QHE and QDP to cover the processes of aggregating protected models and enforcing privacy on the data. Hybrid quantum-classical studies reveal that PQFL is both accurate and advanced in protecting privacy and communication security. According to these results, PQFL works effectively in privacy-friendly domains including healthcare, genomics, and finance, where data needs to be managed securely.