NVFlare: Redefining Collaborative Federated Training
The FCP includes a seamless integration of NVIDIA's Federated Learning framework (NVFlare), enabling you to train machine learning models collaboratively across distributed health data sources. This revolutionary approach brings privacy-preserving training to the forefront, allowing healthcare organizations to pool their insights without compromising the security of sensitive data.
Key Aspects and Advantages
- Secure Distributed Training: NVFlare empowers users to conduct Federated Training across a network of healthcare institutions, each contributing their data insights without sharing raw data. This distributed approach ensures that sensitive patient information remains secure behind institutional firewalls.
- NVIDIA GPU Acceleration: NVFlare taps into the computational prowess of NVIDIA GPUs, expediting model training and optimization. This acceleration is a game-changer, reducing training time and enhancing the accuracy of models trained on massive healthcare datasets.
- Versatility Across ML Frameworks: NVFlare's framework compatibility extends to major machine learning frameworks such as PyTorch and TensorFlow. Adapt your existing machine learning code to NVFlare, ensuring seamless integration into the Federated Learning ecosystem.
Unlocking Secure Federated Training
To harness the capabilities of NVFlare Code Objects within Rhino FCP, follow these steps:
- Adaptation to NVFlare Framework: Align your machine learning code with NVFlare's Federated Learning framework. This step ensures your models are primed to operate collaboratively across distributed data. (see our Rhino "user resources" GitHub repository for examples)
- Container Image Integration: Build a container image that encapsulates your adapted NVFlare code, along with the required dependencies. These images are the vessels for efficient, privacy-preserving training across distributed health data.
- ECR Repository Integration: Push your container images to your workgroup's Elastic Container Registry (ECR) repository. This makes the NVFlare images available within the FCP ecosystem for execution via NVFlare Code Objects.
- Create and Collaborate: Access the Code Objects view within your project on the FCP Dashboard. Create an NVFlare Code Object, specifying your container image and configurations. Collaborate securely across institutions to train ML models and/or run model inference.
- Elevate Federated Training: Launch Federated Training using NVFlare Code Objects, and the FCP takes care of orchestrating the federated network and communication aspects. You can even simulate federated training using multiple datasets from your workgroup.
Championing Secure Insights and Collaborative Progress
NVFlare Code Objects redefine the paradigm of Federated Training, emphasizing secure and collaborative healthcare insights. By utilizing the power of NVIDIA GPUs and preserving data privacy, NVFlare empowers healthcare organizations to collaborate, innovate, and drive transformative advancements in predictive modeling.
Summary
NVFlare Code Objects within Rhino FCP signify a groundbreaking approach to Federated Training. Embrace the synergy of distributed health data, NVIDIA GPU acceleration, and Federated Learning framework compatibility to unlock secure, efficient, and impactful insights. Experience the future of collaborative healthcare research with NVFlare Objects, as you safeguard sensitive data while unlocking the full potential of Federated Training in the digital health landscape.
Actions
- Creating a New NVFlare Code Object or Code Object Version: Create a new NVFlare Code Object or new version of an existing Code Object
- Viewing an NVFlare Code Object Configuration: Viewing the configuration that was provided while creating a specific NVFlare Code Object
- Running an NVFlare Code Object: Running a specific version of a certain NVFlare Code Object
- Deleting an NVFlare Code Object or Code Object Version: Delete a single version or a whole NVFlare Code Object