Set up your system | IBM Cloud Pak for Data as a Service

Set up your system

Last updated: Nov 21, 2024

Set up your system

Before you can use IBM Federated Learning, ensure that you have the required hardware, software, and dependencies.

Core requirements by role

Each entity that participates in a Federated Learning experiment must meet the requirements for their role.

Admin software requirements

Designate an admin for the Federated Learning experiment. The admin must have:

Access to the platform with watsonx.ai Studio and watsonx.ai Runtime enabled.
You must create a watsonx.ai Runtime service instance.
A project for assembling the global model. You must associate the watsonx.ai Runtime service instance with your project.

Party hardware and software requirements

Each party must have a system that meets these minimum requirements.

Note: Remote parties participating in the same Federated Learning experiment can use different hardware specs and architectures, as long as they each meet the minimum requirement.

Supported architectures

Requires 4 GB memory or greater for hardware.

x86 64-bit
Mac M-series

Supported environments

Linux
Mac OS/Unix
Windows

Software dependencies

A supported Python version and a machine learning framework.
The watsonx.ai Runtime Python client.
1. If you are using Linux, run pip install 'ibm_watsonx_ai[fl-rt23.1-py3.10]'.
2. If you are using Mac OS with M-series CPU and Conda:
  1. For runtime 24.1, run pip install ibm_watsonx_ai[fl-rt24.1-py3.11,fl-crypto-rt24.1].
  2. For runtime 23.1, download the installation script and then run ./install_fl_rt23.1_macos.sh <name for new conda environment>.

Network requirements

An outbound connection from the remote party to aggregator is required. Parties can use firewalls that restrict internal connections with each other.

Data sources requirements

Data must comply with these requirements.

Data must be in a directory or storage repository that is accessible to the party that uses them.
Each data source for a federate model must have the same features. IBM Federated Learning supports horizontal federated learning only.
Data must be in a readable format, but the formats can vary by data source. Suggested formats include:
- Hive
- Excel
- CSV
- XML
- Database

Parent topic: Creating a Federated Learning experiment