面向隐私感知联邦生物信号学习的混合量子启发Kolmogorov-Arnold网络

摘要

心电图（ECG）记录是敏感的生物医学数据，限制了医院和可穿戴设备共享原始信号以进行集中式模型训练的能力。联邦学习通过允许在保持原始生物信号数据位于各自来源的同时进行协作式模型训练，从而解决了这一实际隐私约束。然而，由于客户端样本有限、心律失常标签不均衡以及各客户端数据非独立同分布（non-IID），联邦心电图分类仍具挑战性。这些约束要求分类器既具备通信效率，又能对跨客户端分布偏移具有鲁棒性。在本研究中，我们在联邦平均（FedAvg）框架下，评估了混合量子启发柯尔莫戈洛夫-阿诺德网络（HQKAN）与多层感知机（MLP）在MIT-BIH数据集上的五类心律失常分类和INCART数据集上的三分类任务中的表现。在多种客户端配置下，HQKAN在多数总体指标和少数类指标上均有提升，同时在MIT-BIH数据集上可训练参数减少37.35%，通信成本降低24.89%；在INCART数据集上分别减少44.81%和36.41%。这些结果表明，对于生物信号数据上的隐私感知联邦学习，HQKAN为MLP基线提供了一种紧凑、通信高效且鲁棒的替代方案。

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.