Towards Practical Quantum Federated Learning: Enhancing Efficiency and Noise Tolerance

Federated Learning (FL) enables privacy-preserving distributed model training, yet remains vulnerable to gradient inversion and model leakage attacks. Quantum communication has been proposed to provide information-theoretic security for parameter aggregation. However, practical deployment is severely constrained by communication overhead and quantum channel noise. In this work, we present a systematic quantitative study of communication--convergence--noise trade-offs in Quantum Federated Learning (QFL). We introduce two complementary strategies to reduce quantum transmissions: (1) structured parameter reduction based on light-cone feature selection in parametrized quantum circuits, and (2) a Hybrid QFL architecture that dynamically switches from centralized to decentralized aggregation during training. We derive explicit communication cost formulas and show that Hybrid QFL reduces quantum transmissions from 3NMP per round to \{3t + 2(T - t)\}NMP, achieving substantial savings while preserving near-centralized convergence. We further analyze robustness under depolarizing noise and show that decentralized aggregation is more noise-resilient because it transmits fewer qubits per round. Finally, we evaluate the effectiveness of Steane code-based quantum error correction under high-noise regimes. Our results provide an integrated design framework for communication-efficient and noise-aware QFL, clarifying practical trade-offs necessary for scalable quantum-secure distributed learning.