HantaWatch: Federated Learning for Hantavirus Genomic Surveillance
This work addresses the need for privacy-preserving, collaborative genomic surveillance in public health, specifically for hantavirus, but the approach is domain-specific and incremental.
HantaWatch introduces a federated learning framework for hantavirus genomic surveillance that enables collaborative model training across distributed sites without sharing raw data, achieving high-risk screening and outbreak prediction while balancing performance and stability.
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.