对比三款联邦学习框架在医疗影像中的表现，助力实际部署选择。
Benchmarking Federated Learning Frameworks for Medical Imaging Deployment: A Comparative Study of NVIDIA FLARE, Flower, and Owkin Substra
- 用PathMNIST数据集测试三框架的训练效率与通信开销。
- NVIDIA FLARE适合生产级扩展，Flower便于研究原型开发。
- Owkin Substra隐私合规性突出，适合高敏感场景。
联邦学习（FL）已成为医疗AI领域的变革性范式，支持机构间协作训练模型而无需直接共享数据。本研究对三种主流联邦学习框架——NVIDIA FLARE、Flower和Owkin Substra进行基准测试，评估其在真实医疗影像应用场景中的适用性。基于PathMNIST数据集，从模型性能、收敛效率、通信开销、可扩展性及开发者体验等方面展开评估。结果表明，NVIDIA FLARE在生产环境扩展性方面表现优异；Flower在原型设计与学术研究中具有高度灵活性；Owkin Substra展现出卓越的隐私保护与合规能力。各框架各有优势，针对不同使用场景优化，凸显其在医疗领域实际部署的重要价值。
原文摘要 · Abstract (English)
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.
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