The Pragmatic Guide to Federated AI: Building Compliant LLM/XGBoost Pipelines for Sensitive Data

Federated Pipelines for XGBoost and TabNet are a way to federate data models. They can be made practical with the right abstractions. Training AI models in regulated sectors (healthcare, finance) requires Federated Learning (FL) to keep sensitive tabular data local. A secure FL setup uses a Coordinator (for orchestration), Clients (hospitals/banks computing local updates), and an Aggregator.