Federated autoencoder enhances ECG anomaly detection with privacy 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.

RANK_REASON The cluster contains an academic paper detailing a novel system for ECG anomaly detection using federated learning and differential privacy. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• DP-SGD
• Edge Devices
• ECG
• FedAvg
• Federated Autoencoder
• Flower
• GDPR
• HIPAA
• INT8 quantization
• PTB-XL dataset
• Raspberry Pi 4
• Rényi-DP

• paper
• safety
• product

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