Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunities
It tackles data-sharing barriers in bioinformatics for academic and clinical institutions, but is incremental as a review paper.
The paper reviews federated learning applications in bioinformatics, addressing legal and technical challenges to enable multi-institutional data sharing while protecting patient privacy.
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.