Federated foundation models for psychiatry: a new paradigm for diagnosis, prognosis, and treatment of mood disorders
Multimodal Multitask Federated Foundation Models (M3T-FedFMs) represent a new frontier in artificial intelligence (AI), enabling integration of diverse data modalities and multitask learning while preserving data confidentiality through federated learning. Although still in their infancy, these models hold immense promise for advancing psychiatric research, particularly in the characterization and assessment of mood disorders. In this perspective paper, we articulate a forward-looking vision for deploying M3T-FedFMs in psychiatric practice and delineate key challenges and open research directions critical for realizing next-generation, AI-driven mental health care.
Authors
- Bita Akram (ORCID: https://orcid.org/0000-0001-5195-5841)
- Seyyedali Hosseinalipour
- Rajeev Sahay (ORCID: https://orcid.org/0000-0001-6823-1364)
- Maryam Ebrahimi
Institutions
- Sharif University of Technology (IR)
- North Carolina State University (US)
- University of San Diego (US)
- UC San Diego Health System (US)
- North Central State College (US)
- Artificial Intelligence in Medicine (Canada) (CA)
- University at Buffalo, State University of New York (US)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2026-03-13
- DOI
- https://doi.org/10.3389/fpsyt.2026.1792429
- Primary Topic
- Digital Mental Health Interventions
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- Division of Electrical, Communications and Cyber Systems