QuNetQFL is a quantum federated learning protocol using distributed quantum keys for secure aggregation, experimentally validated on a four-client quantum network with scalability simulations to 200 clients and applications to quantum datasets and hybrid language models.
Experimentally validated quantum-secure federated learning over a multi-user quantum network
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abstract
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 towards 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 four-client quantum network and benchmark its performance using the generated keys on quantum and real-world datasets. Adding a single quantum client significantly improves global accuracy for classifying multipartite entangled and non-stabilizer 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.
years
2025 2verdicts
UNVERDICTED 2representative citing papers
AdeptHEQ-FL integrates hybrid CNN-PQC models, adaptive homomorphic encryption, accuracy-weighted aggregation, and dynamic layer freezing in federated learning to gain accuracy on image datasets while lowering communication overhead.
citing papers explorer
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Experimentally validated quantum-secure federated learning over a multi-user quantum network
QuNetQFL is a quantum federated learning protocol using distributed quantum keys for secure aggregation, experimentally validated on a four-client quantum network with scalability simulations to 200 clients and applications to quantum datasets and hybrid language models.
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AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing
AdeptHEQ-FL integrates hybrid CNN-PQC models, adaptive homomorphic encryption, accuracy-weighted aggregation, and dynamic layer freezing in federated learning to gain accuracy on image datasets while lowering communication overhead.