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arXiv cs.LG
arXiv cs.LG
7/21/2026
HantaWatch: Federated Learning for Hantavirus Genomic Surveillance

HantaWatch: Federated Learning for Hantavirus Genomic Surveillance

Short summary

HantaWatch is a federated learning framework enabling labs and surveillance sites to collaboratively train sequence-based models for Hantavirus genomic surveillance without sharing raw data. It integrates k-mer feature extraction, source-aware federated client construction, adaptive DU-FedProx optimization, and prediction-only triage. Experiments show it balances predictive performance, false-negative risk, and update stability across binary and multi-class tasks including high-risk screening and outbreak prediction.

  • •Federated learning framework enables collaborative Hantavirus surveillance without raw data sharing
  • •Combines k-mer features, source-aware clients, and adaptive DU-FedProx optimization
  • •Outputs risk scores, confidence estimates, and ranked expert-review priorities for decision support

Generated with AI, which can make mistakes.

#research-breakthrough#open-source
Read full article at arXiv cs.LG

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