Hybrid Quantum-inspired Kolmogorov-Arnold Networks for Privacy-Aware Federated Biosignal Learning

2026-08-13

AI Summary

This study addresses data heterogeneity, label imbalance, and communication bottlenecks in federated ECG classification by proposing a novel Hybrid Quantum-Inspired Kolmogorov-Arnold Network (QIKAN) integrated with the FedAvg framework. This approach effectively enhances robustness and parameter efficiency for cross-client arrhythmia classification while preserving privacy. Experimental results demonstrate that, compared to traditional MLPs, QIKAN reduces model parameters by 44.81% and communication overhead by 36.41%, while significantly improving classification metrics across most categories. Consequently, this work achieves efficient and precise distributed biosignal learning under strict privacy constraints, offering a promising solution for resource-constrained federated healthcare applications.

Abstract

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.

Problem

• Federated Learning
• ECG Classification
• Non-IID Data
• Communication Efficiency
• Privacy-Aware

Innovation

• Quantum-inspired Kolmogorov-Arnold Networks
• Federated Learning
• Communication Efficiency
• Biosignal Classification
• Privacy-Aware Learning