2. Setup Environment

Before installing and configuring Cifer's FedLearn, it's important to properly prepare your environment. Follow these steps to ensure a smooth setup process:

Update Python

Cifer requires Python 3.8 or later. To check your Python version:

bash

python --version

If Python is not installed or you need to upgrade, download and install the latest version from python.org.

Set Up a Virtual Environment

It's recommended to use a virtual environment to avoid conflicts with other Python projects:

bash

python -m venv cifer_env

Activate the virtual environment:

• On Linux or macOS:

bash

source cifer_env/bin/activate

• On Windows:

bash

cifer_env\Scripts\activate

Upgrade pip

Ensure you have the latest version of pip:

bash

pip install --upgrade pip

Check GPU/TPU Availability (Optional)

If you plan to use GPU or TPU acceleration:

For NVIDIA GPUs:

bash

nvidia-smi

For TPUs (on Google Cloud):

bash

gcloud compute tpus list

Set Up Cloud Environment (for Collaboration Mode)

If you're planning to use FedLearn in collaboration mode:

1. Ensure you have access to a cloud platform (e.g., AWS, Google Cloud, Azure).

2. Set up firewall rules to allow communication on the required ports.

3. Prepare a shared storage solution for model checkpoints (e.g., S3, Google Cloud Storage).

Prepare Your Dataset

Organize your dataset in a format compatible with FedLearn. Typically, this involves:

1. Splitting your data into training and validation sets.

2. Ensuring consistent file formats across all participating nodes.

3. Placing the data in a directory accessible to the FedLearn client.

Review System Requirements

Double-check that your system meets all the requirements listed in the previous section, including RAM, storage, and any specific hardware needs.