Federated learning has emerged as a powerful paradigm for decentralized model training, ensuring privacy preservation by allowing clients to collaboratively learn a shared model without exchanging raw data. Quantum federated learning (QFL) extends this approach by leveraging quantum computing to enhance computational efficiency and security. However, existing QFL frameworks face challenges in handling temporal inconsistencies and ensuring model robustness across time-evolving datasets. Recent findings in quantum physics suggest the emergence of two opposing arrows of time in quantum systems, indicating that time-reversal symmetry can be harnessed for computational processes. This work introduces dual-timeline quantum federated learning (DT-QFL), a novel framework that integrates time-reversal symmetry into QFL. DT-QFL employs quantum memory kernels to encode temporal correlations in client updates, ensuring that both past and future data distributions contribute to the learning process. In addition, we introduce a quantum temporal-invariant neural network, which enables federated models to learn patterns invariant to time flow, improving generalization and reducing catastrophic forgetting in decentralized environments.
Federated learning has emerged as a powerful paradigm for decentralized model training, ensuring privacy preservation by allowing clients to collaboratively learn a shared model without exchanging raw data. Quantum federated learning (QFL) extends this approach by leveraging quantum computing to enhance computational efficiency and security. However, existing QFL frameworks face challenges in handling temporal inconsistencies and ensuring model robustness across time-evolving datasets. Recent findings in quantum physics suggest the emergence of two opposing arrows of time in quantum systems, indicating that time-reversal symmetry can be harnessed for computational processes. This work introduces dual-timeline quantum federated learning (DT-QFL), a novel framework that integrates time-reversal symmetry into QFL. DT-QFL employs quantum memory kernels to encode temporal correlations in client updates, ensuring that both past and future data distributions contribute to the learning process. In addition, we introduce a quantum temporal-invariant neural network, which enables federated models to learn patterns invariant to time flow, improving generalization and reducing catastrophic forgetting in decentralized environments.
Abstract The unstoppable growth of the Industrial Internet of Things (IIoT) already has left the field of outright decentralization of data, provoking an urgent need in the creation of sensitive machine learning models that consider user privacy. The de facto standard towards this goal has been Federated Learning (FL), which allows edge devices to be trained without providing access to raw information. However, the dynamism of IIoT systems with Non-Independent and Identically Distributed (Non-IID) data is expected to worsen the performance of FL, resulting in such problems as model divergence and catastrophic forgetting. Quantum Federated Learning (QFL) solves these difficulties by transferring project data to high dimensional Hilbert spaces with Variational Quantum Circuits (VQCs), which is able to discover correlations that cannot be observed with classical networks. Among the latest inventions in this respect is the usage of TimeReversal Symmetry, another concept of physics which has been used to stabilize the learning process, based on matching the present model states with their preceding ones. The existing methods, such as Dual-Timeline QFL (DT-QFL) are flawed in the sense that they use hyperparameters to weight historical snapshots that are known beforehand. This rigidity may lead to slow convergence and too much communication overhead especially among networks. The present paper suggests a new model called Q-Attentive DT-QFL that implements a Quantum Self-Attention Mechanism (QSAM) in the time-reversal workflow. The model defines the relative values of past data at a certain point in time on its own based on dynamically computing attention scores between the current parameter trajectory and time reversed states. To support our proposal we present in depth theoretical analysis which illustrates convergence limits and reveals that our adaptive weighting methodology forms an important minimization of the variance of global updates. Scaling experiments on the Quantum MNIST dataset have shown that Q -Attentive DT -QFL has the highest accuracy in classification with 94.2 per cent accuracy and consumes forty percent less communication rounds compared with both fixed DT -QFL and classical baselines. b We also perform a detailed security review and certify the resistance of the framework to quantum noise (i.e. Depolarizing and Amplitude Damping ) and also compatibility with NearIntermediate-Scale Quantum (NISQ) hardware. In order to ensure transparency, we project the space of quantum features with SHAP values to explain the mechanisms of decision-making and use Principal Component Analysis(PCA).
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) 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.
In this study, we explore the innovative domain of Quantum Federated Learning (QFL) as a framework for training Quantum Machine Learning (QML) models via distributed networks. Conventional machine learning models frequently grapple with issues about data privacy and the exposure of sensitive information. Our proposed Federated Quantum Neural Network (FedQNN) framework emerges as a cutting-edge solution, integrating the singular characteristics of QML with the principles of classical federated learning. This work thoroughly investigates QFL, underscoring its capability to secure data handling in a distributed environment and facilitate cooperative learning without direct data sharing. Our research corroborates the concept through experiments across varied datasets, including genomics and healthcare, thereby validating the versatility and efficacy of our FedQNN framework. The results consistently exceed 86% accuracy across three distinct datasets, proving its suitability for conducting various QML tasks. Our research not only identifies the limitations of classical paradigms but also presents a novel framework to propel the field of QML into a new era of secure and collaborative innovation.
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.
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.