Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices

Researchers developed a federated learning system for ECG anomaly detection that simultaneously achieves GDPR/HIPAA compliance, real-time edge device performance, and clinical-grade detection accuracy across non-uniform hospital data. The system combines differential privacy, quantization, and federated averaging to enable privacy-preserving cardiac monitoring on resource-constrained hardware like Raspberry Pi 4.

This research addresses a critical intersection of healthcare technology, privacy regulation, and edge computing deployment. The system tackles the genuine challenge of bringing machine learning to clinical settings where patient data cannot be centralized due to legal requirements, yet detection must occur in real-time on constrained devices. The work demonstrates that federated learning architectures can match or exceed centralized baselines while maintaining formal privacy guarantees through differential privacy mechanisms.

The broader context reflects increasing regulatory pressure on healthcare data handling, combined with growing demand for on-device AI inference. Traditional cloud-based medical AI systems face HIPAA and GDPR compliance challenges that create both liability and operational friction. This federated approach emerged as hospitals and healthcare systems seek to leverage machine learning without exposing sensitive patient data or maintaining expensive server infrastructure.

The key technical insight—that differential privacy and quantization penalties are empirically independent—offers practical value to developers. This means practitioners can achieve both strong privacy guarantees and compact models without the typical performance trade-offs that complicate deployment decisions. An ε=4 privacy parameter recommendation provides concrete guidance for clinical applications.

For the healthcare AI sector, this represents incremental but meaningful progress toward deployable systems that satisfy regulatory, performance, and accuracy constraints simultaneously. The work signals that federated learning infrastructure is maturing beyond research demonstrations into practical clinical configurations. However, real-world adoption depends on broader ecosystem factors including hospital IT integration capabilities, regulatory guidance on acceptable privacy parameters, and standardization of federated learning frameworks for healthcare.

• →Federated learning matched or exceeded centralized baseline performance (0.782 AUROC) while preserving patient privacy through distributed training across hospitals.
• →INT8 quantization reduced model size by 50% and edge device latency by up to 44% with negligible accuracy loss, enabling Raspberry Pi 4 deployment.
• →Formal differential privacy (ε=4) and quantization penalties operate independently, allowing simultaneous optimization for privacy and computational efficiency.
• →First system to combine federated learning, formal (ε,δ)-differential privacy, unsupervised detection, and quantized edge deployment for clinical ECG monitoring.
• →Architecture addresses GDPR/HIPAA compliance requirements while enabling real-time inference on resource-constrained hardware without centralizing patient data.