Scaling Enterprise Federated AI with Flower and Open Cluster Management

Scaling Enterprise Federated AI with Flower and Open Cluster Management Flower: The industry-standard for federated AI Flower architecture From server-client to SuperLink-SuperNode ML framework agnosticism Deployment at scale Deploy federated AI at enterprise scale The solution: Open Cluster Management Hub-spoke architecture Key OCM components OCM vs. Flower How the flower-addon integration works The ecosystem value Get started References Flower Documentation OCM Documentation Flower Addon The adaptable enterprise: Why AI readiness is disruption readiness About the authors Meng Yan Chong Shen Ng More like this Enable intelligent insights with Red Hat Satellite MCP Server AI quickstart: Protecting inference with F5 Distributed Cloud and Red Hat AI Technically Speaking | Build a production-ready AI toolbox Technically Speaking | Platform engineering for AI agents Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share Federated AI inverts the traditional machine learning paradigm. Instead of bringing data to the model, it brings the model to the data. Training happens locally on distributed nodes (i. e. , hospitals, banks, and edge devices), and only model updates are shared with a central coordinator. The raw data never leaves its source. We will discuss this approach and how it enables collaborative AI while addressing privacy regulations (i. e. , GDPR-EU data protection and HIPAA-US healthcare privacy) and data sovereignty requirements critical for healthcare, finance, and cross-border deployments. In this post, we show how Flower-combined with Open Cluster Management, the open source foundation of Red Hat Advanced Cluster Management for Kubernetes-provides a production-ready solution for deploying federated AI at enterprise scale. Flower is currently one of the most popular open-source frameworks for federated AI in the world.