arXiv:2511.00037v1 Announce Type: new 
Abstract: Federated Learning (FL) has emerged as a transformative paradigm in medical AI, enabling collaborative model training across institutions without direct data sharing. This study benchmarks three prominent FL frameworks NVIDIA FLARE, Flower, and Owkin Substra to evaluate their suitability for medical imaging applications in real-world settings. Using the PathMNIST dataset, we assess model performance, convergence efficiency, communication overhead, scalability, and developer experience. Results indicate that NVIDIA FLARE offers superior production scalability, Flower provides flexibility for prototyping and academic research, and Owkin Substra demonstrates exceptional privacy and compliance features. Each framework exhibits strengths optimized for distinct use cases, emphasizing their relevance to practical deployment in healthcare environments.

أبرزت دراسة حديثة إمكانيات التعلم الفيدرالي (FL) في إحداث ثورة في الذكاء الاصطناعي الطبي من خلال السماح للمؤسسات بتدريب النماذج بشكل تعاوني دون مشاركة البيانات الحساسة. توضح هذه المقارنة بين ثلاثة أطر FL رائدة—NVIDIA FLARE وFlower وOwkin Substra—فعاليتها في تطبيقات التصوير الطبي. باستخدام مجموعة بيانات PathMNIST، تقيم الدراسة مؤشرات الأداء الرئيسية، مما يمثل خطوة مهمة نحو تحسين تقنيات التصوير الطبي مع ضمان خصوصية البيانات.

Un estudio reciente ha destacado el potencial del Aprendizaje Federado (FL) para revolucionar la IA médica al permitir que las instituciones entren modelos de manera colaborativa sin compartir datos sensibles. Este análisis comparativo de tres marcos FL líderes—NVIDIA FLARE, Flower y Owkin Substra—demuestra su efectividad para aplicaciones de imagen médica. Al utilizar el conjunto de datos PathMNIST, la investigación evalúa métricas clave de rendimiento, lo que representa un paso significativo hacia la mejora de las tecnologías de imagen médica mientras se garantiza la privacidad de los datos.

Une étude récente a mis en lumière le potentiel de l'apprentissage fédéré (FL) pour révolutionner l'IA médicale en permettant aux institutions de former des modèles de manière collaborative sans partager de données sensibles. Cette analyse comparative de trois cadres FL de premier plan—NVIDIA FLARE, Flower et Owkin Substra—démontre leur efficacité pour les applications d'imagerie médicale. En utilisant le jeu de données PathMNIST, la recherche évalue des indicateurs de performance clés, ce qui constitue une avancée significative vers l'amélioration des technologies d'imagerie médicale tout en garantissant la confidentialité des données.

A recent study has highlighted the potential of Federated Learning (FL) in revolutionizing medical AI by allowing institutions to collaboratively train models without sharing sensitive data. This comparative analysis of three leading FL frameworks—NVIDIA FLARE, Flower, and Owkin Substra—demonstrates their effectiveness for medical imaging applications. By utilizing the PathMNIST dataset, the research evaluates key performance metrics, making it a significant step towards enhancing medical imaging technologies while ensuring data privacy.

Benchmarking Federated Learning Frameworks for Medical Imaging Deployment: A Comparative Study of NVIDIA FLARE, Flower, and Owkin Substra

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Benchmarking Federated Learning Frameworks for Medical Imaging Deployment: A Comparative Study of NVIDIA FLARE, Flower, and Owkin Substra

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