Practical Quantum Federated Learning for Privacy-Sensitive Healthcare: Communication Efficiency and Noise Resilience

TL;DR

This paper systematically analyzes quantum federated learning for healthcare, focusing on reducing communication overhead and enhancing noise resilience with novel strategies like parameter reduction and hybrid architectures.

Contribution

It introduces two strategies—light-cone feature selection and a hybrid centralized-decentralized approach—to improve communication efficiency and noise robustness in quantum federated learning.

Findings

Hybrid QFL significantly reduces quantum transmissions while maintaining convergence.

Decentralized aggregation shows higher noise resilience under depolarizing noise.

Quantum error correction methods like Steane code improve performance in high-noise scenarios.

Abstract

AI-driven medical diagnostics increasingly requires collaborative model training across institutions, yet centralizing patient data conflicts with privacy regulations. Federated Learning enables distributed training without raw data sharing, but remains vulnerable to gradient inversion and model leakage attacks. Furthermore, harvest-now-decrypt-later attacks render computationally secure protocols insufficient for protecting long-lived medical records. Quantum communication offers information-theoretic security immune to such threats, making Quantum Federated Learning (QFL) a compelling framework for healthcare. However, practical deployment is constrained by communication overhead and quantum channel noise. We present a systematic quantitative study of communication, convergence, and noise trade-offs in QFL, introducing two complementary strategies to reduce quantum transmissions: (1) structured parameter reduction via light-cone feature selection in parameterized quantum circuits, and (2) a Hybrid QFL architecture that dynamically switches between centralized and decentralized aggregation. We show that Hybrid QFL reduces total quantum transmissions from $3\,TNMP$, the cost of pure Centralized QFL, to $\{3t + 2(T - t)\}\,NMP$ over $T$ rounds while preserving near-centralized convergence. We further demonstrate that decentralized aggregation is more noise-resilient under depolarizing noise, and evaluate Steane code-based quantum error correction in high-noise regimes. Our results provide an integrated design framework for communication-efficient, noise-aware QFL, clarifying practical trade-offs for scalable quantum-secure distributed learning in healthcare.