{"slug": "federated-training-performs-as-well-as-centralized-for-3d-latent-diffusion-for", "title": "Federated training performs as well as centralized for 3D Latent Diffusion for Diagnosis-Conditioned Brain MRI Synthesis", "summary": "A 3D conditional latent diffusion model trained federatedly on multi-site brain MRI data performed similarly to centralized training, according to arXiv paper 2611.08771v1. The authors evaluated conditional fidelity (how well generated MRIs perform in classification), diversity (MultiScale-SSIM match to the real data distribution), and realism (Fréchet Inception Distance), and concluded that federated learning is a viable route to develop multi-site generative models of brain MRI while keeping data private at each site.", "body_md": "arXiv:2611.08771v1 Announce Type: new \nAbstract: Generative models of brain MRI are useful to understand brain conditions, and to enrich scarce and imbalanced neuroimaging data. However, training these models requires large, multi-site training sets, which is challenging. Federated learning (FL) allows for training deep learning models without sharing data. We show that training a 3D conditional latent model federatedly (where data is kept private at each site) performs similarly to centralized training (where all the data have been copied to a single site). We evaluate three metrics: conditional fidelity (how well generated MRIs perform in classification), diversity (how the generated images match the real data distribution, using MultiScale-SSIM), and realism (how close generated images are to real images, using Fr\\'{e}chet Inception Distance). Our results show that federated training is a viable route to develop multi-site generative models of brain MRI.", "url": "https://wpnews.pro/news/federated-training-performs-as-well-as-centralized-for-3d-latent-diffusion-for", "canonical_source": "https://arxiv.org/abs/2611.08771", "published_at": "2026-11-09 05:00:00+00:00", "updated_at": "2026-11-09 05:23:57.945690+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "generative-ai", "computer-vision", "ai-safety"], "entities": ["arXiv", "Fréchet Inception Distance", "MultiScale-SSIM"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/federated-training-performs-as-well-as-centralized-for-3d-latent-diffusion-for", "markdown": "https://wpnews.pro/news/federated-training-performs-as-well-as-centralized-for-3d-latent-diffusion-for.md", "text": "https://wpnews.pro/news/federated-training-performs-as-well-as-centralized-for-3d-latent-diffusion-for.txt", "jsonld": "https://wpnews.pro/news/federated-training-performs-as-well-as-centralized-for-3d-latent-diffusion-for.jsonld"}}