Edge AI today relies on centralizing data from edge devices to the cloud, but this is impractical due to costs and privacy constraints. Federated Learning (FL) is a viable alternative: edge nodes collaboratively train a ML model without transferring or exposing proprietary data. Instead, only model weights are shared, allowing each entity to develop a model that outperforms what it could train independently. Despite its potential, FL is largely academic due to the complexity of integrating expertise across the technology stack. Additionally, decentralized data can be heterogeneous, requiring non-generalizable, application-specific solutions. EdgeLake-FL is a hardware-agnostic framework leveraging EdgeLake, an LF Edge project, to automate the continuous learning FL workflow. With EdgeLake as the data management layer, decentralized data appears centralized and data heterogeneity is resolved. Using EdgeLake-FL, an ML engineer publishes a training application, and Edge nodes with relevant data autonomously train, share, and aggregate models. Each node can then leverage the aggregated models for inference directly at the Edge. In this talk, I will demo EdgeLake-FL in a real use case.