在超低功耗设备上实现隐私保护的心电监测，通信量减少76.7%
Towards Family-Grouped Hierarchical Federated Learning on Sub-5KB Models: A Feasibility Study of Privacy-Preserving ECG Monitoring for Ultra-Resource-Constrained Wearables
- 按家庭分组的三层联邦学习架构，降低通信开销
- 仅669参数模型压缩至4.65KB Flash，准确率达91.9%
- 适合资源受限可穿戴设备，尤其适用于心律失常早期筛查
心血管疾病是全球主要死因，通过可穿戴设备持续监测心电图可早期发现心律失常，预防致命事件。联邦学习（FL）可在不上传原始数据的前提下实现隐私保护协同训练，但传统方法通信开销大，深度模型无法部署于超低功耗微控制器。本文提出家族分组层级联邦学习（Family-FL），以家庭为自然隐私边界，在全局同步前进行家庭内聚合。设计仅含669参数的硬件约束型Tiny CNN-LSTM，经INT8量化后仅占4.65KB Flash和2.95KB RAM，满足STC32G12K128类微控制器要求。在MIT-BIH心律失常数据库上的实验（5次独立运行平均结果）表明，Family-FL相比FedAvg通信量减少76.7%，且保持相当精度；Family-FL-Tiny实现91.9±1.2%准确率与0.483±0.031宏F1，总通信量仅为FedAvg的0.31%。模型对室性心律失常检测性能优异（单类F1=0.80），是居家初筛中最关键的异常类型。模拟评估验证了在超资源受限微控制器上实现隐私保护联邦学习的技术可行性。研究亦坦诚指出局限：无硬件部署、仅单一数据集验证（MIT-BIH，47名受试者）、罕见类别敏感度下降，以及缺乏正式差分隐私保障。
原文摘要 · Abstract (English)
Cardiovascular disease remains the leading cause of death worldwide, and early detection of arrhythmias through continuous ECG monitoring on wearable devices can prevent life-threatening events. Federated Learning (FL) enables privacy-preserving collaborative training by keeping raw ECG data on device, yet standard FL incurs prohibitive communication overhead and standard deep learning models cannot fit on ultra-low-power microcontrollers. We propose Family-Grouped Hierarchical Federated Learning (Family-FL), a three-tier architecture that uses the family as a natural privacy boundary for intra-family aggregation before global synchronization. We further design a hardware-constrained Tiny CNN-LSTM architecture with only 669 parameters, INT8-quantized to occupy merely 4.65KB Flash and 2.95KB RAM, meeting the constraints of STC32G12K128-class microcontrollers. Experiments on the MIT-BIH Arrhythmia Database (mean of 5 independent runs with different seeds) demonstrate that Family-FL reduces communication volume by 76.7% compared to FedAvg while maintaining comparable accuracy. Family-FL-Tiny achieves 91.9 +/- 1.2% accuracy with macro-F1 of 0.483 +/- 0.031, reducing total communication to 0.31% of FedAvg. The model achieves reliable ventricular arrhythmia detection (per-class F1 = 0.80), the most clinically critical abnormality for home-based preliminary screening. These results demonstrate the technical feasibility of privacy-preserving federated learning on ultra-resource-constrained microcontrollers through simulation-based evaluation. We honestly discuss limitations: no hardware deployment, single-dataset validation (MIT-BIH, 47 subjects), reduced rare-class sensitivity, and absence of formal differential privacy guarantees.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。