1. Preparation

In the following sections, we'll guide you through the process of setting up and using FHE, from installation to core feature usage. Whether you're a data owners, data scientist, machine learning engineer, or decision-maker in a data-driven organization, FHE provides the tools and framework to implement secure, privacy-preserving AI workflows.

System Requirements

To run FHE effectively, your system should meet the following requirements:

• Operating System:

  • Linux (Ubuntu 18.04 or later)

  • MacOS (10.14 or later)

  • Windows 10

• Python: Version 3.9

• RAM: Minimum 8GB, 16GB or more recommended for larger models

• Storage: At least 30GB of free disk space

• Network: Stable internet connection for collaboration mode

GPU Requirements (Optional)

FHE supports two main types of GPU acceleration: NVIDIA CUDA and Google's Tensor Processing Units (TPUs). Choose the option that best fits your hardware and requirements.

NVIDIA CUDA

If you're using NVIDIA GPUs:

• CUDA-capable GPU: NVIDIA GPU with compute capability 3.5 or higher

• CUDA Toolkit: Version 10.1 or later (11.8 recommended for optimal performance)

• cuDNN: Version 7.6 or later

• NVIDIA GPU Drivers: Compatible with your CUDA Toolkit version

To verify your GPU is CUDA-capable:

bash

lspci | grep -i nvidia

To check CUDA version:

bash

nvcc --version

Tensor Processing Units (TPUs)

If you're using Google Cloud TPUs:

• TPU Hardware: Access to a TPU device or TPU Pod

• TensorFlow: Version 2.3.0 or later with TPU support

• Google Cloud Account: Active account with TPU quota

• Cloud Storage Bucket: For storing model checkpoints and data

To check TPU availability in TensorFlow:

python

import tensorflow as tf
print(tf.config.list_physical_devices('TPU'))