PERSPECTIVE article
Front. Psychiatry
Sec. Mood Disorders
Federated Foundation Models for Psychiatry: A New Paradigm for Diagnosing, Prognosis, and Treatment of Mood Disorders
Provisionally accepted
- 1Sharif University of Technology, Tehran, Iran
- 2University of California San Diego, La Jolla, United States
- 3NC State University, Raleigh, United States
- 4University at Buffalo, Buffalo, United States
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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.
Keywords: Federated learning, Foundation models, Mental Health, Multimodal multitask learning, Psychiatry
Received: 20 Jan 2026; Accepted: 12 Feb 2026.
Copyright: © 2026 Ebrahimi, Sahay, Akram and Hosseinalipour. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Seyyedali Hosseinalipour
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