Variational quantum algorithm (VQA) accesses the centralized data to train the model, and using distributed computing can significantly improve the training overhead; however, the data is privacy sensitive. In this paper, we propose communication-efficient learning of VQA from decentralized data, which is so-called quantum federated learning (QFL). Motivated by the classical federated learning algorithm, we improve data privacy by aggregating updates from local computation to share model parameters. Here, aiming to find approximate optima in the parameter landscape, we develop an extension of the conventional VQA. Finally, we deploy on the TensorFlow Quantum processor within variational quantum tensor networks classifiers, approximate quantum optimization for the Ising model, and variational quantum eigensolver for molecular hydrogen. Our algorithm demonstrates model accuracy from decentralized data, which have higher performance on near-term processors. Importantly, QFL may inspire new investigations in the field of secure quantum machine learning.
Variational quantum algorithm (VQA) accesses the centralized data to train the model, and using distributed computing can significantly improve the training overhead; however, the data is privacy sensitive. In this paper, we propose communication-efficient learning of VQA from decentralized data, which is so-called quantum federated learning (QFL). Motivated by the classical federated learning algorithm, we improve data privacy by aggregating updates from local computation to share model parameters. Here, aiming to find approximate optima in the parameter landscape, we develop an extension of the conventional VQA. Finally, we deploy on the TensorFlow Quantum processor within variational quantum tensor networks classifiers, approximate quantum optimization for the Ising model, and variational quantum eigensolver for molecular hydrogen. Our algorithm demonstrates model accuracy from decentralized data, which have higher performance on near-term processors. Importantly, QFL may inspire new investigations in the field of secure quantum machine learning.
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 machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. Recently, some purely quantum machine learning models were proposed such as the quantum convolutional neural networks (QCNN) to perform classification on quantum data. However, all of the existing QML models rely on centralized solutions that cannot scale well for large-scale and distributed quantum networks. Hence, it is apropos to consider more practical quantum federated learning (QFL) solutions tailored towards emerging quantum network architectures. Indeed, developing QFL frameworks for quantum networks is critical given the fragile nature of computing qubits and the difficulty of transferring them. On top of its practical momentousness, QFL allows for distributed quantum learning by leveraging existing wireless communication infrastructure. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner. First, given the lack of existing quantum federated datasets in the literature, the proposed framework begins by generating the first quantum federated dataset, with a hierarchical data format, for distributed quantum networks. Then, clients sharing QCNN models are fed with the quantum data to perform a classification task. Subsequently, the server aggregates the learnable quantum circuit parameters from clients and performs federated averaging. Extensive experiments are conducted to evaluate and validate the effectiveness of the proposed QFL solution. This work is the first to combine Google's TensorFlow Federated and TensorFlow Quantum in a practical implementation.
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.
AI-driven medical diagnostics increasingly requires collaborative model training across institutions, yet centralizing patient data conflicts with privacy regulations. Federated Learning enables distributed training without raw data sharing, but remains vulnerable to gradient inversion and model leakage attacks. Furthermore, harvest-now-decrypt-later attacks render computationally secure protocols insufficient for protecting long-lived medical records. Quantum communication offers information-theoretic security immune to such threats, making Quantum Federated Learning (QFL) a compelling framework for healthcare. However, practical deployment is constrained by communication overhead and quantum channel noise. We present a systematic quantitative study of communication, convergence, and noise trade-offs in QFL, introducing two complementary strategies to reduce quantum transmissions: (1) structured parameter reduction via light-cone feature selection in parameterized quantum circuits, and (2) a Hybrid QFL architecture that dynamically switches between centralized and decentralized aggregation. We show that Hybrid QFL reduces total quantum transmissions from 3TNMP, the cost of pure Centralized QFL, to {3t+2(T−t)}NMP over T rounds while preserving near-centralized convergence. We further demonstrate that decentralized aggregation is more noise-resilient under depolarizing noise, and evaluate Steane code-based quantum error correction in high-noise regimes. Our results provide an integrated design framework for communication-efficient, noise-aware QFL, clarifying practical trade-offs for scalable quantum-secure distributed learning in healthcare.
Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore complex machine learning problems. Recently, some QML models were proposed for performing classification tasks, however, they rely on centralized solutions that cannot scale well for distributed quantum networks. Hence, it is apropos to consider more practical quantum federated learning (QFL) solutions tailored towards emerging quantum networks to allow for distributing quantum learning. This paper proposes the first fully quantum federated learning frame-work that can operate over purely quantum data. First, the proposed framework generates the first quantum federated dataset in literature. Then, quantum clients share the learning of quantum circuit parameters in a decentralized manner. Extensive experiments are conducted to evaluate and validate the effectiveness of the proposed QFL solution, which is the first implementation combining Google’s TensorFlow Federated and TensorFlow Quantum.
In the healthcare sector, the scarcity of data and privacy concerns present formidable challenges to the widespread adoption of machine learning. In the present-day scenario, Federated Learning (FL) emerges as a pivotal solution, fostering the rapid evolution of distributed machine learning paradigms while adeptly addressing the problem of data governance and privacy. It allows distributed clients to collaboratively train a global model by synchronizing their local updates without sharing private data. In recent years, federated learning and quantum computing have individually shown great promise to revolutionize various sectors, including healthcare, finance, and manufacturing, where privacy protection is paramount. In this article, we propose a communication-efficient Quantum Federated Learning (QFL) framework based on a variational circuit that enables clients to efficiently train and transmit quantum model parameters, thereby reducing communication rounds significantly and enhancing QFL performance using quantum natural gradient descent (QNGD) optimization. This paper demonstrates the feasibility of a QFL framework for predicting the presence of coronary heart disease, diagnosing whether a patient is suffering from diabetes or not, and differentiating malignant and benign cancer by distributing the UCI datasets unbalanced among healthcare institutions. The proposed framework has the potential to incorporate privacy, security, and the expedited processing of distributed data. QNGD outperformed classical GD by reducing communication rounds by a range of 5% to 60%. In addition to reducing the communication rounds by optimizing the QFL training algorithm and achieving quicker convergence, it also determines the important features regardless of the data imbalance among the clients.