arXiv Computer Vision October 12, 2026 By Xiao Yang, Dengbo He, Duoxun Tang, Jiyao Wang, Kaishun Wu

FedHUG: Federated Heterogeneous Unsupervised Generalization for Remote Physiological Measurements

Read the original on arXiv Computer Vision →

FedHUG introduces a Federated Unsupervised Domain Generalization protocol to address privacy and labeling challenges in remote physiological measurement. The framework includes a Minimal Bias Aggregation module that adapts weights to heterogeneous non‑IID features and a Global Distribution‑aware Learning Controller that adjusts client training to mitigate label skew and long‑tail issues. Experiments demonstrate superior performance over state‑of‑the‑art methods using RGB video or mmWave radar data.

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