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This paper evaluates a hybrid quantum-inspired Kolmogorov-Arnold network (HQKAN) for federated learning in ECG classification, addressing the challenges of limited client samples, imbalanced labels, and non-IID data. The HQKAN outperforms a multilayer perceptron (MLP) in aggregate and minority-class metrics while significantly reducing the number of trainable parameters and communication costs across multiple client configurations. These findings highlight HQKAN as a more efficient and robust solution for privacy-aware biosignal learning compared to traditional methods.
HQKAN achieves superior ECG classification performance while cutting down trainable parameters and communication costs by over 37%鈥攁 game changer for privacy-sensitive federated learning.
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.
