Getting set up for Federated Learning
Before you can get started with Federated Learning, you must plan out some of the components.
Cloud open beta
This is a Cloud open preview and is not supported for use in production environments.
Here is what you need to set up before proceeding with Federated Learning.
- Federated Learning automatically comes with the IBM Watson Machine Learning service. See: Setting up your environment for IBM Watson Machine Learning.
- Create a project. To learn more about projects, see Projects.
- Federated Learning is designed to connect data from multiple parties but at least one party must exist with:
- A remote server where they run their local training that can be connected to a Remote Training System. To view the requirements for being able to connect to the Remote Training System, see Remote server requirements.
- Data to be used for training on their remote training system. The data can be of a wide variety of formats, including databases, CSV files.
- Download the Federated Learning Python SDK, which includes these necessary files for training:
- A party configuration
yml
file - A training handler file
- A party configuration
- All parties involved must be collaborators to the Watson Studio project where the Federated Learning experiment exists.
- Note: For all parties, they must follow the steps documented in Anaconda configuration to set up their Anaconda environment.
Next step
Now you can get started with Federated Learning.