FL_K8S

Implementing K8S with Jetson TX2 for Federated Learning with Adaptive Dataset Management

Prerequisites

Ensure the following prerequisites are met:

• Jetson TX2 flashed with JetPack SDK.
• Kubernetes (K8s) installed.

Environment

• Master Node (Server)
• Worker Nodes (Jetson TX2)

Steps

Follow these steps to set up and run Federated Learning (FL) with Kubernetes (K8s) on Jetson TX2:

1. Clone Repository

Clone the FL_K8S repository on each Jetson TX2 (Worker Node):

# Clone the repository
git clone https://github.com/your-username/FL_K8S.git
cd FL_K8S

2. Build Docker Image on Jetson

On each Jetson TX2 (Worker Node), execute the following steps to build the Docker image required for FL:

# Navigate to the directory containing your Dockerfile
cd path/to/your/Dockerfile

# Build the Docker image
docker build -t fl_image .

Upload the built image to the docker hub.

3. Deploy Kubernetes Manifests on Master Node

On the Master Node (Server), apply the Kubernetes manifests to deploy the FL components:

# Apply the Kubernetes manifests
kubectl apply -f your-manifest.yaml

4. Execute run.py on Master Node

On the Master Node (Server), execute the run.py script to start the Federated Learning process:

# Execute the run.py script
python run.py

Additional Notes

• Ensure proper network connectivity between the Master Node and Worker Nodes for seamless communication.
• Monitor the Kubernetes pods to ensure they are running correctly using the kubectl get pods command.
• Customize the YAML manifests according to your FL application's requirements.