Privacy-Preserving ECG-HRV Emotion Classification for Mood Swing Detection using Federated Learning on Constrained Edge Devices

Researchers propose a federated learning approach for ECG-HRV (heart rate variability) based emotion classification and mood swing detection that preserves privacy by training models on edge devices without centralizing sensitive patient data. The method addresses key constraints of healthcare deployments: data privacy compliance, reduced bandwidth requirements, and computational efficiency on resource-limited devices.

Researchers propose a federated learning approach for ECG-HRV (heart rate variability) based emotion classification and mood swing detection that preserves privacy by training models on edge devices without centralizing sensitive patient data. The method addresses key constraints of healthcare deployments: data privacy compliance, reduced bandwidth requirements, and computational efficiency on resource-limited devices.

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