Towards Practical Quantum Federated Learning: Enhancing Efficiency and Noise Tolerance

• URL: http://arxiv.org/abs/2603.03853v1
• Date: Wed, 04 Mar 2026 09:04:10 GMT
• Title: Towards Practical Quantum Federated Learning: Enhancing Efficiency and Noise Tolerance
• Authors: Suzukaze Kamei, Hideaki Kawaguchi, Takahiko Satoh,
• Abstract summary: Federated Learning (FL) enables privacy-preserving distributed model training, yet remains vulnerable to gradient inversion and model leakage attacks.<n>Quantum communication has been proposed to provide information-theoretic security for parameter aggregation.<n>We present a systematic quantitative study of communication--convergence--noise trade-offs in Quantum Federated Learning.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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