Advances in Privacy Preserving Federated Learning to Realize a Truly Learning Healthcare System
The concept of a learning healthcare system (LHS) envisions a self-improving network where multimodal data from patient care are continuously analyzed to enhance future healthcare outcomes. However, realizing this vision faces significant challenges in data sharing and privacy protection. Privacy-Preserving Federated Learning (PPFL) is a transformative and promising approach that has the potential to address these challenges by enabling collaborative learning from decentralized data while safeguarding patient privacy. This paper proposes a vision for integrating PPFL into the healthcare ecosystem to achieve a truly LHS as defined by the Institute of Medicine (IOM) Roundtable.
Authors
- Ravi Madduri (ORCID: https://orcid.org/0000-0003-2130-2887)
- Kibaek Kim (ORCID: https://orcid.org/0000-0002-5820-6533)
- Zilinghan Li (ORCID: https://orcid.org/0000-0002-0160-2519)
- Tarak Nandi (ORCID: https://orcid.org/0000-0002-7050-6619)
- Minseok Ryu (ORCID: https://orcid.org/0000-0002-1954-7722)
- Alex C. Rodriguez (ORCID: https://orcid.org/0000-0002-0213-6695)
Institutions
- Argonne National Laboratory (US)
- Arizona State University (US)
Publication Details
- Published
- 2024-10-28
- DOI
- https://doi.org/10.1109/tps-isa62245.2024.00039
- Citations
- 4
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- U.S. Department of Energy
- Office of Science
- Advanced Scientific Computing Research