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

TL;DR

This study compares NVIDIA FLARE, Flower, and Owkin Substra frameworks for federated learning in medical imaging, evaluating performance, scalability, privacy, and developer experience to guide real-world healthcare deployment.

Contribution

It provides a comprehensive benchmarking of three major federated learning frameworks tailored for medical imaging, highlighting their respective strengths and use cases.

Findings

NVIDIA FLARE has superior scalability for production environments.

Flower offers flexibility suitable for research and prototyping.

Owkin Substra excels in privacy and compliance features.

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.