🌸 AIDE Federated Learning for Robust, Resilient and Adaptive Protection of Systems 🌸

Welcome to the repository of the AIDE federated learning project! 🚀

📚 Project Overview

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🚀 Getting Started

📂 Cloning the Repository

Start by cloning this repository to your local machine or a designated testbed server:

git clone <repository-url>

🛠️ Setting Up the Environment

To set up your environment, you should aim to use the Docker containers locally or to run the system on a (managed) k8s instance.

Option 1: Using Docker Containers

Docker provides a consistent environment that simplifies dependency management. Follow these steps to set up using Docker:

1. Pull Docker Images

   Pull the pre-built Docker images for both the server and clients from the registry:

   docker pull [TODO build & push cnl image to github] // Server
   
   docker pull [TODO build & push cnl image to github] // Client

2. Run Server Container

   Start the server in a Docker container, exposing the necessary ports and mounting volumes if required:

   docker run -p 8080:8080 -v /path/to/data:/app/dataset --network host gitlab.ilabt.imec.be:4567/aide-fl/aide-infra/server \
   --experiment <experiment-name> \
   --server 0.0.0.0:8080

   This command runs the server, maps port 8080, and mounts the data directory from the host to the container.

3. Run Client Containers

   To connect clients to the server, run the client containers:

   docker run -v /path/to/data:/app/dataset --network host gitlab.ilabt.imec.be:4567/aide-fl/aide-infra/client \
   --cid <client-id> \
   --experiment <experiment-name> \
   --server <server-address>

   Use --network host to ensure proper network configuration for local testing. Adjust the server address as needed.

Option 2: Setting up on k8s

If you prefer to not run the Docker containers locally, but deploy them directly onto k8s then there are two options.

Option 1 (recommended): CloudNativeLab

Option 2: local k8s (microk8s, kubeadm)

📊 Monitoring and Analysis

Using TensorBoard

Monitor training progress and visualize metrics with TensorBoard:

tensorboard --logdir=logs

This however is not available for every experiment ❗

With these options, you can choose the setup method that best fits your needs. Enjoy exploring Federated Learning with Flower! 🌸