IBM Federated Learning

How Federated Learning works

Watch this overview video to learn the basic concepts and elements of a Federated Learning experiment. Learn how you can apply the tools for your company's analytics enhancements.

An example for using Federated Learning is when an aviation alliance wants 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 do so either in application silos or any other scenario where regulatory or pragmatic considerations prevent users from sharing data. The resulting model benefits each member of the alliance with improved business insights while lowering risk from data migration and privacy issues.

As the following graphic illustrates, parties can be geographically distributed and run on different platforms. One member might work on a physical server with a relational database in North America, another member works with a virtual machine and Cloud Object Storage in Europe, and the last member might work in Australia with their laptop and a CSV file.

Why use IBM Federated Learning

IBM Federated Learning has a wide range of applications 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.
• Complies with data privacy and security while training models with different data sources.

Learn more

• Federated Learning tutorials and samples
  • Federated Learning Tensorflow tutorial for UI
  • Federated Learning Tensorflow samples for API
  • Federated Learning XGBoost tutorial for UI
  • Federated Learning XGBoost sample for API
  • Federated Learning homomorphic encryption sample for API
• Getting started
  • Terminology
  • Federated Learning architecture
• Frameworks, fusion methods, and Python versions
  • Hyperparameter definitions
• Creating a Federated Learning experiment
  • Set up your system
  • Creating the initial model
  • Create the data handler
  • Starting the aggregator (Admin)
  • Connecting to the aggregator (Party)
  • Monitoring and saving the model
• Applying encryption
• Limitations and troubleshooting

Parent topic: Analyzing data and building models