用於隱私感知聯邦生物訊號學習的混合量子啟發 Kolmogorov-Arnold 網路

摘要

心電圖（ECG）記錄是敏感的生物醫學資料，限制了醫院與穿戴式裝置共享原始訊號以進行集中式模型訓練的能力。聯邦學習透過在各自來源端保留原始生物訊號資料的同時，實現協作式模型訓練，解決了此實際的隱私限制。然而，由於客戶端樣本有限、心律不整標籤不平衡，以及客戶端之間的資料呈非獨立同分佈（non-IID），聯邦式心電圖分類仍具挑戰性。這些限制要求分類器具備通訊效率與對跨客戶端分佈偏移的穩健性。在本研究中，我們在聯邦平均法（FedAvg）下，針對MIT-BIH資料集的五類別心律不整分類與INCART資料集的三類別分類，評估了混合量子啟發的Kolmogorov-Arnold網路（HQKAN）與多層感知器（MLP）的表現。在多重客戶端配置下，HQKAN改善了大部分整體及少數類別指標，同時在MIT-BIH上使用的可訓練參數減少了37.35%，通訊成本降低了24.89%；在INCART上則分別達成44.81%與36.41%的對應降幅。這些結果顯示，HQKAN為生物訊號資料上的隱私感知聯邦學習，提供了相較於MLP基準更精簡、通訊效率更高且更穩健的替代方案。

English

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.