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.
Federated learning with hierarchical clustering of local updates to improve training on non-iid data
3 Pith papers cite this work. Polarity classification is still indexing.
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FedGMI applies VAEs as density estimators in federated learning to infer mixture proportions of shared distributions for structured personalization under data heterogeneity.
The paper surveys technical requirements, use cases, challenges, and future trends for building brain-computer interfaces on top of 6G wireless networks.
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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.