Work

Federated foundation models for psychiatry: a new paradigm for diagnosis, prognosis, and treatment of mood disorders

Year: 2026

Type: article

Abstract: 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.

Source: Frontiers in Psychiatry

DOI: 10.3389/fpsyt.2026.1792429

Authors: Maryam Ebrahimi, Rajeev Sahay, Bita Akram, Seyyedali Hosseinalipour

Institutions: Sharif University of Technology, University of California San Diego, North Carolina State University, 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

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,801