Benchmarking Federated Learning Frameworks for Medical Imaging Deployment: A Comparative Study of NVIDIA FLARE, Flower, and Owkin Substra
This comparative analysis helps healthcare practitioners and researchers select appropriate federated learning tools for medical imaging deployment, though it is incremental as it evaluates existing frameworks on standard data.
The study benchmarked three federated learning frameworks (NVIDIA FLARE, Flower, and Owkin Substra) for medical imaging, finding that NVIDIA FLARE excels in scalability, Flower in flexibility, and Owkin Substra in privacy features.
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.