IBM Federated Learning

Federated Learning provides the tools for training a model collaboratively, by coordinating local data training and fusing the results. The data sources are never moved, combined, or shared, but they each contribute to training and improving the quality of the global model.

Service The Watson Studio, Watson Machine Learning, Watson OpenScale, and other supplemental services are not available by default. An administrator must install these services on the IBM Cloud Pak for Data platform. To determine whether a service is installed, open the Services catalog and check whether the service is enabled.

Federated Learning is appropriate for any situation where different entities from different countries and Cloud providers, such as banks or airlines, want to use their data to train an analytical model without sharing their data.

For example, an aviation alliance might want to model how a global pandemic impacts airline delays. Each participating party in the federation can use their data to train a common model without ever moving or sharing their data. They can use Federated Learning to do so either in application silos or any other scenario where regulatory or pragmatic considerations prevent users from moving data to one place. The resulting model benefits each member of the alliance with improved business insights while lowering risk from data migration and privacy issues.

IBM Federated Learning provides the means to:

• Discover different data sources.
• Configure and deploy a Federated Learning experiment.
• Coordinate local data training, and allows models to be fused without data exposure.

Why use IBM Federated Learning

IBM Federated Learning has a wide range of application across many enterprise industries. Federated Learning:

• Enables sites with large volumes of data to be collected, cleaned, and trained on an enterprise scale without migration.
• Accommodates for the differences in data format, quality, and constraints.
• Comply with data privacy and security while training models with different data sources.

How Federated Learning works

• See Creating the Federated Learning experiment for:
  • A brief conceptual overview of Federated Learning.
  • High-level steps on how to get started with creating a Federated Learning experiment within a Watson Machine Learning platform.
• See the Federated Learning Tutorials and Samples for a step-by-step walkthrough of the service.
  • Federated Learning Tensorflow 2 tutorial
  • Federated Learning XGBoost tutorial
  • Federated Learning Tensorflow 2 samples

Additional resources

• To read more about practical application for Federated Learning, see the blog post "Data, data everywhere...": Leveraging IBM Watson Studio for private data with Federated Learning.
• For in-depth technical insight on more details of Federated Learning components, see Federated Learning whitepaper.
• Federated Learning provides a list of helper functions to facilitate the data preparation process. See the API documentation to learn more about using Federated Learning APIs.

Parent topic: Analyzing data and building models