联邦学习下无监督域泛化，实现远程生理信号精准测量
FedHUG: Federated Heterogeneous Unsupervised Generalization for Remote Physiological Measurements
- 通过动态加权聚合缓解多源异构数据偏差
- 解决标签分布不均与长尾问题，提升跨域泛化能力
- 适用于无标注用户数据的实时模型更新，适合医疗健康场景
远程生理测量受到广泛关注，但需收集敏感隐私信息，且现有非接触式测量仍依赖带标签客户端数据。当需要利用大量无标签用户数据更新已部署模型时，面临挑战。为此，本文提出新型联邦无监督域泛化（FUDG）协议，并构建联邦异构无监督泛化（FedHUG）框架：(1) 最小偏差聚合模块基于先验驱动的偏差评估动态调整聚合权重，应对多领域异构非独立同分布特征；(2) 全局分布感知学习控制器参数化标签分布，动态调节客户端训练策略，缓解服务器-客户端标签分布偏移与长尾问题。在基于RGB视频或毫米波雷达的估计任务中，该方法优于现有主流技术。代码将公开。
原文摘要 · Abstract (English)
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.
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