QUBO formulation on quantum annealers for joint client selection in federated learning, combined with a MultiSignal routing ensemble, yields higher Byzantine attack detection accuracy than MultiKrum on challenging attacks at both small and moderate scales.
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Hybrid QFL cuts quantum transmissions from 3TNMP to {3t + 2(T-t)}NMP over T rounds while preserving near-centralized convergence and improving depolarizing-noise resilience via decentralized aggregation and Steane-code QEC.
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.
citing papers explorer
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Byzantine-Resilient Federated Learning via QUBO-Based Client Selection on Quantum Annealers
QUBO formulation on quantum annealers for joint client selection in federated learning, combined with a MultiSignal routing ensemble, yields higher Byzantine attack detection accuracy than MultiKrum on challenging attacks at both small and moderate scales.
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Practical Quantum Federated Learning for Privacy-Sensitive Healthcare: Communication Efficiency and Noise Resilience
Hybrid QFL cuts quantum transmissions from 3TNMP to {3t + 2(T-t)}NMP over T rounds while preserving near-centralized convergence and improving depolarizing-noise resilience via decentralized aggregation and Steane-code QEC.
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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.