{"ID":2855541,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.12132","arxiv_id":"2510.12132","title":"FedHUG: Federated Heterogeneous Unsupervised Generalization for Remote Physiological Measurements","abstract":"Remote physiological measurement gained wide attention, while it requires collecting users' privacy-sensitive information, and existing contactless measurements still rely on labeled client data. This presents challenges when we want to further update real-world deployed models with numerous user data lacking labels. To resolve these challenges, we instantiate a new protocol called Federated Unsupervised Domain Generalization (FUDG) in this work. Subsequently, the \\textbf{Fed}erated \\textbf{H}eterogeneous \\textbf{U}nsupervised \\textbf{G}eneralization (\\textbf{FedHUG}) framework is proposed and consists of: (1) Minimal Bias Aggregation module dynamically adjusts aggregation weights based on prior-driven bias evaluation to cope with heterogeneous non-IID features from multiple domains. (2) The Global Distribution-aware Learning Controller parameterizes the label distribution and dynamically manipulates client-specific training strategies, thereby mitigating the server-client label distribution skew and long-tail issue. The proposal shows superior performance across state-of-the-art techniques in estimation with either RGB video or mmWave radar. The code will be released.","short_abstract":"Remote physiological measurement gained wide attention, while it requires collecting users' privacy-sensitive information, and existing contactless measurements still rely on labeled client data. This presents challenges when we want to further update real-world deployed models with numerous user data lacking labels. T...","url_abs":"https://arxiv.org/abs/2510.12132","url_pdf":"https://arxiv.org/pdf/2510.12132v2","authors":"[\"Xiao Yang\",\"Dengbo He\",\"Jiyao Wang\",\"Kaishun Wu\"]","published":"2025-10-14T04:17:25Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}