How to scale enterprise federated AI with Flower and OCM

Federated AI enables training machine learning models across distributed nodes without centralizing raw data, addressing privacy regulations like GDPR and HIPAA. Flower is the leading open-source federated AI framework, used by organizations like Samsung, JP Morgan, and the NHS. However, deploying Flower at enterprise scale introduces operational challenges around deployment, security, scheduling, and lifecycle management. Open Cluster Management (OCM), a CNCF Sandbox project powering Red Hat Advanced Cluster Management, provides a natural solution due to its hub-spoke architecture that mirrors Flower's SuperLink/SuperNode topology. The flower-addon integration bridges both systems: OCM's addon framework handles SuperNode deployment with automatic TLS certificate management, the Placement API enables dynamic scheduling based on cluster labels and resources, and ManifestWorkReplicaSet automates ClientApp distribution across the fleet. This declarative approach integrates with existing GitOps workflows and allows organizations to scale federated AI across hundreds of clusters without manual per-cluster configuration.