Robust Federated Video-based Remote Physiological Measurement for Heterogeneous Multi-source Data
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View abstract on PubMed
Summary
This summary is machine-generated.Federated learning (FL) addresses privacy and data transmission costs in remote photoplethysmography (rPPG). Our FedGRC framework tackles multi-source rPPG data heterogeneity for improved non-contact physiological measurements.
Area Of Science
- Biomedical Engineering
- Computer Science
- Signal Processing
Background
- Remote photoplethysmography (rPPG) enables non-contact physiological monitoring using facial videos.
- Current rPPG methods face challenges with data transmission costs and privacy concerns.
- Federated learning (FL) offers a solution but struggles with cross-domain heterogeneity in multi-source rPPG data.
Purpose Of The Study
- To address the challenges of privacy and cross-domain heterogeneity in federated training for remote photoplethysmography.
- To propose a novel federated learning framework, FedGRC, for effective multi-source rPPG data analysis.
- To characterize the heterogeneity of multi-source rPPG data from both input and output domain perspectives.
Main Methods
- Characterized multi-source rPPG data heterogeneity from input and output domains.
- Introduced pseudo-labeling techniques within a federated learning framework.
- Developed FedGRC with automatic gradient regularization calibration and pseudo-label alignment using handcrafted rPPG methods.
Main Results
- FedGRC effectively mitigates input domain discrepancies through gradient regularization calibration.
- Pseudo-labeling aligns output domains and reduces label inconsistencies across datasets.
- Validated across six public datasets, FedGRC demonstrated significant advantages over existing approaches.
Conclusions
- The FedGRC framework successfully addresses privacy protection and heterogeneity challenges in multi-source rPPG data.
- This approach enhances the feasibility of federated learning for non-contact physiological measurements.
- FedGRC offers a robust solution for privacy-preserving and heterogeneous rPPG data analysis.
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