Privacy-Preserving Federated Autoencoder for ECG Anomaly Detection on Edge Devices
Researchers have developed a privacy-preserving federated autoencoder system for detecting anomalies in electrocardiogram (ECG) data on edge devices. The system combines federated learning with differential privacy and INT8 quantization to maintain patient confidentiality, enable real-time inference on constrained hardware like the Raspberry Pi 4, and achieve high detection quality even with non-IID data from different hospitals. The study found that federated learning matched or surpassed centralized baselines, and INT8 quantization significantly reduced model size and latency with minimal loss in accuracy, demonstrating that privacy and edge deployment can be achieved simultaneously. AI
IMPACT Enables privacy-preserving AI for sensitive health data on resource-constrained devices, potentially accelerating clinical adoption.