Work

A perspective on federated foundation models in biomedical sensing and imaging

Year: 2026

Type: article

Abstract: 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.

Source: npj Digital Medicine

DOI: 10.1038/s41746-026-03164-6

Authors: Koushani Chakrabarty, Seyyedali Hosseinalipour, Leslie Ying

Institution: University at Buffalo, State University of New York

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $4,090

Funders: National Science Foundation, National Institutes of Health, National Institute of Arthritis and Musculoskeletal and Skin Diseases, Division of Electrical, Communications and Cyber Systems