- SYN-Y2-2025-018: Abstract [Asti et al., IRCCS Humanitas Research Hospital
- Blood, journal of the American Society of Hematology, November 2025. Conference: ASH2025. Read here >
- This study evaluates whether federated learning (FL) combined with generative AI can produce high-fidelity synthetic data (SD) for rare hematological diseases while preserving patient privacy. Using a multi-institutional simulation on a myelodysplastic syndromes (MDS) cohort of 4,427 patients distributed across three sites, the authors trained several generative models (CTGAN, Bayesian Networks, VAE-BGM) under multiple FL strategies and assessed data quality using the SAFE validation framework. FL-generated SD closely matched real data, achieving high statistical and clinical fidelity (CSF 0.942; GSF 0.902 by round 5), comparable to centralized training and superior to isolated node training. Privacy was consistently protected, with NNDR metrics showing low re-identification risk. Clinical utility was confirmed through preserved genomic associations, mutation frequencies, and survival patterns, supporting applications such as risk stratification and biomarker discovery. Overall, the study shows that federated SD generation enables secure, scalable, and high-quality data synthesis across institutions without data sharing, offering a promising solution for collaborative research and precision medicine in rare hematological diseases.
Related Impact Highlights
Discover outputs and activities connected to this use case, highlighting the research, collaboration, and dissemination efforts driving progress in SYNTHIA.
