federated-ecg-wearable

name: federated-ecg-wearable description: Federated Learning methodology for privacy-preserving ECG monitoring on ultra-resource-constrained wearable devices. Family-grouped hierarchical FL for sub-5KB cardiovascular models. Activation: federated learning ECG, wearable cardiac monitoring, privacy-preserving ML, sub-5KB model, arrhythmia detection.

Federated ECG Monitoring on Wearables

Overview

Based on arXiv:2605.18862 — studies feasibility of family-grouped hierarchical federated learning for sub-5KB models deployed on ultra-resource-constrained wearable devices for early arrhythmia detection through continuous ECG monitoring.

Cardiovascular disease is the leading cause of death worldwide. Continuous ECG monitoring on wearables can prevent life-threatening events, but privacy and resource constraints limit deployment.

Core Methodology

Three-Level Hierarchy

Individual Device (Sub-5KB) → Family Group Aggregation → Global Server

Device Level: Tiny models (<5KB) trained locally on wearable ECG data
Family Group: Aggregation among devices sharing physiological/genetic similarity
Global: Final model aggregation across family groups

Why Family Grouping?

Family members share cardiac physiology patterns → faster convergence
Reduces non-IID data problem in federated learning
Privacy-preserving: only model weights shared, no raw ECG data
Smaller aggregation groups → less communication overhead

Implementation Pattern

# Device-level training (sub-5KB model)
model = TinyECGClassifier(params=<5000)  # Ultra-compact architecture

for batch in local_ecg_stream:
    loss = model.train(batch)
    model.update_gradients(loss)

# Family-group aggregation
family_updates = collect_family_weights(device_ids)
family_model = weighted_average(family_updates, weights=device_confidence)

# Global aggregation
global_model = federated_avg(family_models, method='hierarchical')

Resource Constraints

Constraint	Target	Rationale
Model size	<5KB	On-device SRAM limits on microcontrollers
Inference latency	<100ms	Real-time arrhythmia detection requirement
Communication	Compressed gradients	Bandwidth-limited wearable connectivity
Energy	<1mW average	Battery life for multi-day wear

Key Advantages

Privacy: No raw ECG leaves the device
Personalization: Family grouping captures shared physiology
Scalability: Hierarchical reduces global communication bottleneck
Accessibility: Sub-5KB models run on commodity wearables
Clinical value: Early arrhythmia detection from continuous monitoring

When to Use

Wearable cardiac monitoring systems
Privacy-sensitive medical ML on edge devices
Federated learning with physiological data
Ultra-low-resource embedded ML deployment

Pitfalls

Sub-5KB models have limited capacity — careful architecture selection needed
Family grouping requires device registration logic
Non-IID data across families may cause bias
Model compression may lose subtle arrhythmia patterns
Communication failures on wearable devices require fault-tolerant aggregation