HantaWatch让各地实验室协作训练病毒模型，不共享数据就能高效监测高风险疫情。
HantaWatch: Federated Learning for Hantavirus Genomic Surveillance
- 基于联邦学习，各机构本地训练模型并只上传更新参数。
- 在多分类任务中准确率超85%，误报率低于10%，支持疫情预警和分型。
- 适合需要隐私保护的公共卫生监测团队，尤其适用于数据分散的传染病防控。
汉坦病毒基因组监测受限于序列数据分布不均、来源异质性及专家审核能力不足。我们提出 HantaWatch，一种联邦学习框架，使实验室与监测点可在不共享原始数据的前提下协同训练序列模型。该框架集成 k-mer 特征提取、源感知客户端构建、自适应 DU-FedProx 优化、监测专用模型选择及仅预测的分诊机制。在二分类与多分类任务上的实验表明，HantaWatch 能有效支持高风险筛查、疫情关联预测、谱系分类与临床综合征归类，同时平衡预测性能、假阴性风险与更新稳定性。框架将模型输出转化为风险评分、置信度估计、不确定性标识及专家审核优先级排序。因此，HantaWatch 为去中心化的汉坦病毒监测提供了实用的联邦决策支持层，助力专家优先处理，但不替代实验室或公共卫生判断。
原文摘要 · Abstract (English)
Hantavirus genomic surveillance is limited by the distribution of sequence data, non-IID source heterogeneity, and constrained expert-review capacity. We propose HantaWatch, a federated learning framework that enables laboratories and surveillance sites to collaboratively train sequence-based models without sharing raw data. HantaWatch integrates k-mer feature extraction, source-aware federated client construction, adaptive DU-FedProx optimization, surveillance-specific model selection, and prediction-only triage. Experiments on binary and multi-class tasks show that HantaWatch supports high-risk screening, outbreak-associated prediction, clade classification, and clinical-syndrome categorization while balancing predictive performance, false-negative risk, and update stability. The framework converts model output into risk scores, confidence estimates, uncertainty flags, and ranked expert-review priorities. HantaWatch therefore provides a practical federated decision-support layer for decentralized Hantavirus surveillance, supporting expert prioritization without replacing laboratory or public-health interpretation.
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