Towards Federated Learning Across Biobanks: Prototype Software from the 2026 Carnegie Mellon University–NVIDIA Hackathon

2026/03/20 by James Mu, Aditya Kumar Karna, Telaprolu Kumar Koushik +78 · 1 voice
Engineering · Biochemistry, Genetics and Molecular Biology · Medicine · #Biomedical and Engineering Education #Cancer Genomics and Diagnostics #Artificial Intelligence in Healthcare and Education

paper · doi:10.37044/osf.io/5psfj_v1

openalex publication_date 2026/03/20 · openalex created_date 2026/03/21 · openalex updated_date 2026/07/15

Abstract

The Carnegie Mellon University-NVIDIA Federated Learning Hackathon for Biomedical Applications (January 7-9, 2026) convened researchers from academia, government, and industry to implement federated frameworks for disease subtyping, genetic association studies, and multimodal clinical prediction using NVIDIA FLARE. This preprint presents ten projects spanninggenome-wide association analyses, histopathology harmonization, pangenome construction, ancestry deconvolution, rare disease stratification, cancer subtyping, polygenic risk score aggregation, and multimodal fusion. These proofs of principle collectively demonstrate both the versatility of federated learning for biomedical applications and the technical considerations required for successful deployment.

Discussions

• "Towards Federated Learning Across Biobanks: Prototype Software from the 2026 Carnegie Mellon University–NVIDIA Hackathon" https://doi.org/10.37044/osf.io/5psfj_v1 "The Carnegie Mellon University-NVI [bsky, 0 points, 0 comments]

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