Federated Learning promises privacy-preserving AI, but it introduces massive operational complexity. How do you train models across distributed, heterogeneous environments (edge, multi-cloud) without a central orchestration layer? This technical deep dive explores using cloud-native tools to build a robust federated learning platform. We will architect a solution using Kubernetes-native patterns to manage distributed training jobs, handle data locality, and securely aggregate model updates. This session will cover architectural patterns for orchestrating federated tasks, managing state, and ensuring the reliability of a system designed for privacy and decentralization. This is for engineers and architects building the next generation of privacy-first ML infrastructure.