A perspective on federated foundation models in biomedical sensing and imaging
In this perspective paper, we introduce multimodal multi-task federated foundation models (M3T FedFMs) as a paradigm for privacy-preserving and distributed learning over biomedical sensing and imaging data. We outline their architecture, applications across medical sectors, key challenges, and future research directions, as well as the metrics and datasets that can facilitate their benchmarking.
Authors
- Leslie Ying (ORCID: https://orcid.org/0000-0001-9801-1362)
- Seyyedali Hosseinalipour
- Koushani Chakrabarty
Institutions
- University at Buffalo, State University of New York (US)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2026-09-01
- DOI
- https://doi.org/10.1038/s41746-026-03164-6
- Primary Topic
- Ultrasound Imaging and Elastography
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- National Institutes of Health
- National Institute of Arthritis and Musculoskeletal and Skin Diseases
- Division of Electrical, Communications and Cyber Systems