Hybrid Quantum-inspired Kolmogorov-Arnold Networks for Privacy-Aware Federated Biosignal Learning
Abstract
A hybrid quantum-inspired Kolmogorov-Arnold network improves federated ECG classification accuracy with fewer parameters and lower communication costs than standard multilayer perceptrons.
Electrocardiogram (ECG) recordings are sensitive biomedical data, limiting the ability of hospitals and wearable devices to share raw signals for centralized model training. Federated learning addresses this practical privacy constraint by enabling collaborative model training while keeping raw biosignal data at their respective sources. However, federated ECG classification remains challenging due to limited client-side samples, imbalanced arrhythmia labels, and non-independent and identically distributed (non-IID) data across clients. These constraints require classifiers that are both communication-efficient and robust to cross-client distribution shifts. In this work, we evaluate a hybrid quantum-inspired Kolmogorov-Arnold network (HQKAN) against a multilayer perceptron (MLP) for five-class arrhythmia classification on the MIT-BIH dataset and three-class classification on the INCART dataset under federated averaging (FedAvg). Across multiple client configurations, HQKAN improves most aggregate and minority-class metrics while using 37.35% fewer trainable parameters and reducing communication cost by 24.89% on MIT-BIH; on INCART, it achieves corresponding reductions of 44.81% and 36.41%. These results indicate that HQKAN offers a compact, communication-efficient and robust alternative to the MLP baseline for privacy-aware federated learning on biosignal data.
Community
This paper investigates HQKAN as a compact classifier for federated ECG analysis, allowing hospitals and wearable devices to collaboratively train models while keeping raw biosignals local. Across the MIT-BIH and INCART datasets, HQKAN consistently improves macro-F1, Cohen’s κ, calibration, and robustness to non-IID client distributions compared with an MLP baseline. At the same time, it reduces trainable parameters by 37.35%–44.81% and per-round communication costs by 24.89%–36.41%. These results highlight the potential of quantum-inspired architectures for communication-efficient and robust healthcare AI at the intersection of federated learning, biosignal processing, and quantum-inspired machine learning.
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