Accurate and Private Diagnosis of Rare Genetic Syndromes from Facial Images with Federated Deep Learning

• URL: http://arxiv.org/abs/2509.10635v1
• Date: Fri, 12 Sep 2025 18:42:33 GMT
• Title: Accurate and Private Diagnosis of Rare Genetic Syndromes from Facial Images with Federated Deep Learning
• Authors: Ali Burak Ünal, Cem Ata Baykara, Peter Krawitz, Mete Akgün,
• Abstract summary: We introduce a service based on a cross-silo horizontal federated learning framework to train a global ensemble feature extractor.<n>Patient data are mapped into a shared latent space, and a privacy-preserving kernel matrix computation framework enables syndrome inference and discovery.<n>Experiments show that the federated service retains over 90% of centralized performance and remains robust to both varying silo numbers and heterogeneous data distributions.
• Score: 0.8399688944263842
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning has shown promise in facial dysmorphology, where characteristic facial features provide diagnostic clues for rare genetic disorders. GestaltMatcher, a leading framework in this field, has demonstrated clinical utility across multiple studies, but its reliance on centralized datasets limits further development, as patient data are siloed across institutions and subject to strict privacy regulations. We introduce a federated GestaltMatcher service based on a cross-silo horizontal federated learning framework, which allows hospitals to collaboratively train a global ensemble feature extractor without sharing patient images. Patient data are mapped into a shared latent space, and a privacy-preserving kernel matrix computation framework enables syndrome inference and discovery while safeguarding confidentiality. New participants can directly benefit from and contribute to the system by adopting the global feature extractor and kernel configuration from previous training rounds. Experiments show that the federated service retains over 90% of centralized performance and remains robust to both varying silo numbers and heterogeneous data distributions.

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