
QuantumFed: A Federated Learning Framework for Collaborative Quantum Training
With the fast development of quantum computing and deep learning, quantu...
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Federated Quantum Machine Learning
Distributed training across several quantum computers could significantl...
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FLRA: A Reference Architecture for Federated Learning Systems
Federated learning is an emerging machine learning paradigm that enables...
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Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition
We propose a novel decentralized feature extraction approach in federate...
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Distributed Learning for Timevarying Networks: A Scalable Design
The wireless network is undergoing a trend from "onnection of things" to...
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Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations
Dimensionality Reduction is a commonly used element in a machine learnin...
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Machine learning for longdistance quantum communication
Machine learning can help us in solving problems in the context big data...
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Quantum Federated Learning with Quantum Data
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 largescale 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.
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