ADP-QFL adaptively adds client-level DP noise to QCNN federated updates, but its central variance-reduction theorem is not correctly derived and its non-convex convergence bound is vacuous.
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Enhancing Gradient Variance and Differential Privacy in Quantum Federated Learning
ADP-QFL adaptively adds client-level DP noise to QCNN federated updates, but its central variance-reduction theorem is not correctly derived and its non-convex convergence bound is vacuous.