IBM Federated Learning

Federated Learning provides the tools for multiple remote parties to collaboratively train a single machine learning model without sharing data. Each party trains a local model with a private data set. Only the local model is sent to the aggregator to improve the quality of the global model that benefits all parties.

Service This service is not available by default. An administrator must install this service on the IBM Cloud Pak for Data platform, and you must be given access to the service. To determine whether the service is installed, open the Services catalog and check whether the service is enabled.

Required service
    Watson Machine Learning. You must install the Watson Machine Learning service instance in Cloud Pak for Data as a Service to use Federated Learning. Federated Learning is available when you install Watson Machine Learning.
Data format
    Any data format including but not limited to CSV files, JSON files, and databases for PostgreSQL.

How Federated Learning works

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.

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
• 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
• Troubleshoot
• Configure the party yml file

Parent topic: Analyzing data and building models