Quantum Federated Learning (QFL) integrates the principles of quantum mechanics with classical machine learning to address the privacy challenges in distributed learning settings. This study introduces a Quantum Enhanced Federated Learning framework with Differential Privacy (QE-FLDP), designed to enhance privacy protections by leveraging quantum mechanics principles. The framework uses a quantum feature engineering circuit that encodes classical data into high-dimensional quantum states through parameterized quantum gates, including Rx rotations and CNOT gates. To ensure privacy, Differential Privacy is incorporated via the Laplace mechanism to safeguard data during the training process. Additionally, the framework integrates Quantum Homomorphic Encryption (QHE), enabling secure computations on encrypted data without exposing the underlying information. Model updates are transmitted over quantum channels post-training, preserving data integrity and ensuring compliance with privacy standards. The QE-FLDP framework was evaluated on synthetic datasets, achieving an average Mean Squared Error (MSE) of 0.0143 and an R2 score of 0.987 over 100 training rounds. These results highlight the potential of quantum principles to improve both model accuracy and data privacy in distributed learning settings.
Quantum Federated Learning (QFL) integrates the principles of quantum mechanics with classical machine learning to address the privacy challenges in distributed learning settings. This study introduces a Quantum Enhanced Federated Learning framework with Differential Privacy (QE-FLDP), designed to enhance privacy protections by leveraging quantum mechanics principles. The framework uses a quantum feature engineering circuit that encodes classical data into high-dimensional quantum states through parameterized quantum gates, including Rx rotations and CNOT gates. To ensure privacy, Differential Privacy is incorporated via the Laplace mechanism to safeguard data during the training process. Additionally, the framework integrates Quantum Homomorphic Encryption (QHE), enabling secure computations on encrypted data without exposing the underlying information. Model updates are transmitted over quantum channels post-training, preserving data integrity and ensuring compliance with privacy standards. The QE-FLDP framework was evaluated on synthetic datasets, achieving an average Mean Squared Error (MSE) of 0.0143 and an R2 score of 0.987 over 100 training rounds. These results highlight the potential of quantum principles to improve both model accuracy and data privacy in distributed learning settings.
Quantum federated learning (QFL) enables collaborative training of quantum machine learning (QML) models across distributed quantum devices without raw data exchange. However, QFL remains vulnerable to adversarial attacks, where shared QML model updates can be exploited to undermine information privacy. In the context of noisy intermediate-scale quantum (NISQ) devices, a key question arises: How can inherent quantum noise be leveraged to enforce differential privacy (DP) and protect model information during training and communication? This paper explores a novel DP mechanism that harnesses quantum noise to safeguard quantum models throughout the QFL process. By tuning noise variance through measurement shots and depolarizing channel strength, our approach achieves desired DP levels tailored to NISQ constraints. Simulations demonstrate the framework's effectiveness by examining the relationship between differential privacy budget and noise parameters, as well as the trade-off between security and training accuracy. Additionally, we demonstrate the framework's robustness against an adversarial attack designed to compromise model performance using adversarial examples, with evaluations based on critical metrics such as accuracy on adversarial examples, confidence scores for correct predictions, and attack success rates. The results reveal a tunable trade-off between privacy and robustness, providing an efficient solution for secure QFL on NISQ devices with significant potential for reliable quantum computing applications.
The exponential expansion of data-driven technologies underscores the critical need for robust privacy safeguards. This study introduces an innovative privacy-preserving framework that combines Differential Privacy (DP) and Federated Learning (FL) with the cutting-edge capability of quantum computing. Leveraging Quantum Random Number Generators (QRNGs), the framework incorporates truly random quantum-generated noise into the differential privacy mechanism, substantially enhancing the security of gradient updates within machine learning models. This noise is specifically added to the gradient updates during the optimization process, using various optimizers. Quantum mechanics' inherent randomness ensures unpredictability, fortifying defenses against sophisticated attacks. This research signifies a significant step towards the development of secure and ethical AI technologies. By integrating quantum computing's random number generation capabilities into privacy-preserving mechanisms, the research paves the way for enhanced data security across various sectors. The findings indicate a promising path for further investigation and experimentation, with potential applications in fields demanding high-stakes data protection. This fusion of quantum computing and privacy-preserving techniques not only enhances the confidentiality of sensitive information but also sets a new standard for privacy in the age of data-driven innovation.
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 preservation of privacy is a critical concern in the implementation of artificial intelligence on sensitive training data. There are several techniques to preserve data privacy but quantum computations are inherently more secure due to the nocloning theorem, resulting in a most desirable computational platform on top of the potential quantum advantages. There have been prior works in protecting data privacy by Quantum Federated Learning (QFL) and Quantum Differential Privacy (QDP) studied independently. However, to the best of our knowledge, no prior work has addressed both QFL and QDP together yet. Here, we propose to combine these privacy-preserving methods and implement them on the quantum platform, so that we can achieve comprehensive protection against data leakage (QFL) and model inversion attacks (QDP). This implementation promises more efficient and secure artificial intelligence. In this paper, we present a successful implementation of these privacy-preservation methods by performing the binary classification of the Cats vs Dogs dataset. Using our quantum-classical machine learning model, we obtained a test accuracy of over 0.98, while maintaining epsilon values less than 1.3. We show that federated differentially private training is a viable privacy preservation method for quantum machine learning on Noisy Intermediate-Scale Quantum (NISQ) devices.
Recent advancements in Quantum Neural Networks (QNNs) have demonstrated theoretical and experimental performance superior to their classical counterparts in a wide range of applications. However, existing centralized QNNs cannot solve many real-world problems because collecting large amounts of training data to a common public site is time-consuming and, more importantly, violates data privacy. Federated Learning (FL) is an emerging distributed machine learning framework that allows collaborative model training on decentralized data residing on multiple devices without breaching data privacy. Some initial attempts at Quantum Federated Learning (QFL) either only focus on improving the QFL performance or rely on a trusted quantum server that fails to preserve data privacy. In this work, we propose CryptoQFL, a QFL framework that allows distributed QNN training on encrypted data. CryptoQFL is (1) secure, because it allows each edge to train a QNN with local private data, and encrypt its updates using quantum homomorphic encryption before sending them to the central quantum server; (2) communication-efficient, as CryptoQFL quantize local gradient updates to ternary values, and only communicate non-zero values to the server for aggregation; and (3) computation-efficient, as CryptoQFL presents an efficient quantum aggregation circuit with significantly reduced latency compared to state-of-the-art approaches.
Quantum Federated Learning (QFL) promises to revolutionize distributed machine learning by combining the computational power of quantum devices with collaborative model training. Yet, privacy of both data and models remains a critical challenge. In this work, we propose a privacy-preserving QFL framework where a network of n quantum devices trains local models and transmits them to a central server under a multi-layered privacy protocol. Our design leverages Singular Value Decomposition (SVD), Quantum Key Distribution (QKD), and Analytic Quantum Gradient Descent (AQGD) to secure data preparation, model sharing, and training stages. Through theoretical analysis and experiments on contemporary quantum platforms and datasets, we demonstrate that the framework robustly safeguards data and model confidentiality while maintaining training efficiency.