Technical Insights and Legal Considerations for Advancing Federated Learning in Bioinformatics
By: Daniele Malpetti , Marco Scutari , Francesco Gualdi and more
Potential Business Impact:
Lets doctors share medical secrets safely.
Federated learning leverages data across institutions to improve clinical discovery while complying with data-sharing restrictions and protecting patient privacy. 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 the same impact in bioinformatics, allowing access to many combinations of genotypic, phenotypic and environmental information that are undercovered or not included in existing biobanks. This paper reviews the methodological, infrastructural and legal issues that academic and clinical institutions must address before implementing it. Finally, we provide recommendations for the reliable use of federated learning and its effective translation into clinical practice.
Similar Papers
Towards Privacy-Preserving Data-Driven Education: The Potential of Federated Learning
Machine Learning (CS)
Keeps student data private while still learning.
Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence
Machine Learning (CS)
Trains computers together without sharing private info.
Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration
Machine Learning (CS)
Keeps your private data safe when sharing.