Q-RAIL derives effective noise budgets from backend metadata and transpiled circuits to produce stabilized aggregation weights for heterogeneous QFL, yielding +10 accuracy points over FedAvg on MNIST under hardware skew.
Fedfa: Federated feature augmentation,
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Q-RAIL: A Reliability-Aware Framework for Quantum Federated Learning on Heterogeneous Noisy Hardware
Q-RAIL derives effective noise budgets from backend metadata and transpiled circuits to produce stabilized aggregation weights for heterogeneous QFL, yielding +10 accuracy points over FedAvg on MNIST under hardware skew.