Founding Collection · Open

Privacy-preserving AI that learns without exposing patients.

JFPAI publishes federated learning, differential privacy, secure computation and synthetic-data methods that let clinical and biomedical AI advance under strict data protection.

Founding Collection — full APC waiver. Priority editorial handling with a target median of 35 days to first decision, an optional, author-approved graphical abstract in the journal style, and front-page visibility. How to take part →
For the founding phase, manuscripts and expressions of interest may be submitted by email to jfpai@vallensis.org. Full portal submission is available on request.

In scope

Federated LearningDifferential PrivacySecure ComputationSynthetic Health DataGDPR-compliant AI

This journal publishes on a continuous basis — articles appear online immediately upon acceptance, without waiting for issue or volume compilation.