Enhanced Security for Federated Learning on GKE Clusters

Question

Pulumi · Accepted Answer

Federated learning is a machine learning approach where a shared model is trained across multiple decentralized devices holding local data samples, without the need to exchange them. Google Kubernetes Engine (GKE) offers a platform to deploy a federated learning system, and operating such a system on GKE would require robust security measures to ensure that sensitive data remains protected.

Enhanced security for a federated learning system on GKE can be addressed by implementing the following:

1. **Private Clusters**: A private cluster in GKE ensures that the nodes have no public IP addresses and can only be accessed within the VPC network or via a private connection from your on-premise network.

2. **Workload Identity**: This Google-recommended approach for GKE authentication helps you to provide granular access to different Google Cloud services directly from the pods that run your Kubernetes workloads.

3. **Binary Authorization**: This service provides software supply-chain security for images that you deploy in GKE, ensuring that the only images that meet your organization’s security requirements are allowed to run.

4. **Shielded GKE Nodes**: These nodes are virtual machines hardened by a suite of security features that defend against rootkits and bootkits.

5. **Role-Based Access Control (RBAC)**: It is used to control access to the Kubernetes API, where you can define roles with specific permissions and assign them to users or groups.

6. **Pod Security Policies (PSP)**: Through PSPs (being deprecated in version 1.25), you can control security sensitive aspects of pod specification to enforce best security practices.

Here's a Pulumi Python program that sets up a secure GKE cluster utilizing some of the security practices mentioned above—specifically, creating a private GKE cluster with Workload Identity enabled:

```python
import pulumi
import pulumi_gcp as gcp

# Create a GCP network for the GKE cluster
network = gcp.compute.Network("gke-network")

# Create a subnetwork for the GKE cluster nodes
subnetwork = gcp.compute.Subnetwork(
    "gke-subnetwork",
    ip_cidr_range="10.2.0.0/16",
    network=network.id,
    region="us-central1"
)

# Create a GKE cluster with enhanced security settings
cluster = gcp.container.Cluster(
    "secure-gke-cluster",
    location="us-central1",
    initial_node_count=1,
    network=network.id,
    subnetwork=subnetwork.id,
    private_cluster_config=gcp.container.ClusterPrivateClusterConfigArgs(
        enable_private_nodes=True,
        enable_private_endpoint=False,
    ),
    workload_identity_config=gcp.container.ClusterWorkloadIdentityConfigArgs(
        workload_pool="PROJECT_ID.svc.id.goog"
    ),
    remove_default_node_pool=True,
    # Ensure the node version matches the cluster master version
    initial_cluster_version="1.18.12-gke.1210",
    # Enable Shielded Nodes for enhanced security
    shielded_nodes=gcp.container.ClusterShieldedNodesArgs(
        enabled=True
    ),
    # Define RBAC for fine-grained access control
    master_authorized_networks_config=gcp.container.ClusterMasterAuthorizedNetworksConfigArgs(
        cidr_blocks=[gcp.container.ClusterMasterAuthorizedNetworksConfigCidrBlocksArgs(
            cidr_block="10.100.0.0/16"
        )]
    ),
    # Replace PROJECT_ID with your GCP project ID
    project="PROJECT_ID"
)

# Export the cluster name and its endpoint
pulumi.export("cluster_name", cluster.name)
pulumi.export("cluster_endpoint", cluster.endpoint)
```

This program will provision a GKE cluster in the `us-central1` region with the following security enhancements:

- A private GKE cluster is defined by `enable_private_nodes=True`, which ensures that your nodes are not given public IP addresses. The `enable_private_endpoint=False` setting allows the master node (which runs the Kubernetes API server) to retain its public endpoint, making it reachable from the public internet but protected by authorized networks.

- Workload Identity is configured through the `workload_identity_config` parameter, specifying the `workload_pool` that is used for IAM bindings to the Kubernetes service accounts.

- Shielded GKE nodes protect against root-level compromises by enabling the Shielded Nodes feature with `enabled=True`.

- Role-Based Access Control (RBAC) for the master network endpoint is set via `master_authorized_networks_config`, where only CIDR blocks listed are permitted to communicate with the master.

- The default node pool is removed with `remove_default_node_pool=True` so additional node pools with specific configurations can be added later.

- A node version is defined that should match the cluster master version for compatibility.

Replace `PROJECT_ID` with your GCP project ID to ensure Workload Identity functions correctly.

This program sets the foundation for a secure environment suitable for sensitive applications like federated learning. Keep in mind that this is the starting point, and you should continue to implement other security best practices specific to your application needs.