Federated Learning System Enables Privacy-Preserving ECG Anomaly Detection on Edge Devices
Researchers have developed and evaluated an end-to-end federated learning system for unsupervised ECG anomaly detection that simultaneously satisfies privacy regulations, real-time edge inference, and cross-hospital data heterogeneity. The system combines autoencoder architectures, differentially private training, and 8-bit quantization, benchmarked on a Raspberry Pi 4 using the PTB-XL dataset across ten simulated hospitals. The work is notable as the first reported system to jointly integrate federated learning, formal differential privacy, unsupervised reconstruction-based detection, and quantized deployment on ARM hardware.
A team of researchers has designed and evaluated a federated learning pipeline for continuous 12-lead ECG anomaly detection that addresses three simultaneous clinical deployment constraints: GDPR/HIPAA-compliant privacy, real-time inference on constrained hardware, and robust performance under non-IID data distributions across hospitals. The system tests three autoencoder families—VanillaAE, ConvAE, and a Variational Autoencoder (VAE)—within a Flower-based federated averaging (FedAvg) framework simulating ten hospitals. Client-side differentially private stochastic gradient descent (DP-SGD) with a Rényi-DP accountant provides formal (ε,δ)-DP guarantees, with ε=4 identified as the recommended clinical operating point. Federated training matched or exceeded centralized baselines, with the ConvAE achieving an AUROC of 0.782. INT8 post-training quantization approximately halved model size and reduced Raspberry Pi 4 inference latency by up to 44%, with less than 0.12% AUROC degradation. Critically, the privacy and quantization penalties were found to be empirically independent, meaning practitioners do not face a trade-off between strong privacy guarantees and a compact edge footprint. The preprint has been submitted to the FLTA 2026 workshop and has not yet undergone peer review.
What's missing
The study simulates ten hospitals using a single public dataset (PTB-XL) with artificially partitioned non-IID splits, rather than real federated deployments across distinct institutions; generalizability to genuine multi-site clinical environments is unvalidated. The paper does not report statistical significance tests or confidence intervals for AUROC comparisons between federated and centralized baselines. Long-term drift, concept shift over time, and adversarial robustness of the federated system in real clinical settings are not addressed. As a preprint, these findings have not yet been peer-reviewed.
What different sources said
- arXiv cs.AICenter
Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices
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