Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunities

• URL: http://arxiv.org/abs/2503.09649v4
• Date: Sun, 09 Nov 2025 22:47:36 GMT
• Title: Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunities
• Authors: Daniele Malpetti, Marco Scutari, Francesco Gualdi, Jessica van Setten, Sander van der Laan, Saskia Haitjema, Aaron Mark Lee, Isabelle Hering, Francesca Mangili,
• Abstract summary: Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy.<n>This paper is the first to review key applications in genome-wide association studies (GWAS), single-cell and multi-omics studies in their legal and infrastructural challenges.
• Score: 0.34953784594970894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. This paper provides a gentle introduction to this approach in bioinformatics, and is the first to review key applications in proteomics, genome-wide association studies (GWAS), single-cell and multi-omics studies in their legal as well as methodological and infrastructural challenges. As the evolution of biobanks in genetics and systems biology has proved, accessing more extensive and varied data pools leads to a faster and more robust exploration and translation of results. More widespread use of federated learning may have a similar impact in bioinformatics, allowing academic and clinical institutions to access many combinations of genotypic, phenotypic and environmental information that are undercovered or not included in existing biobanks.

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