Human-in-the -Loop Data Engineering for Federated AI: Enabling Trust in model Pipelines

The wide adoption of Federated Artificial Intelligence (FAI) in privacy-sensitive and decentralized environments has made trustworthy model development an essential matter. FAI enables distributed model collaboration without sharing centralized data but struggles with data variations and biased algorithms and obscurity in model processes. This paper implements a Human-in-the-Loop (HITL) Data Engineering framework which includes expert human engagement from preprocessing data to evaluating the models throughout the entire federated learning process. The framework incorporates iterative data testing alongside anomaly tracking mechanisms which domain experts use to develop context-dependent feature attributes. Through human feedback integration in federated training cycles the approach improves distributed node quality, model interpretability and trustworthiness. Experimental tests reveal better model convergence along with diminished bias propagation along with enhanced fairness across real-world dataset applications. The approach establishes a framework which enables human-AI collaborative work to sustain transparent and ethically commensurate reliable pipeline operations in dynamic regulated application domains.