Federated Learning Meets Data Engineering: Building Trustworthy AI with Decentralized Data Pipelines
Bhanuprakash Madupati, Anil Kumar Jonnalagadda, Santosh Kumar Vududala, Rohith Varma Vegesna · 2025
The increasing requirement for private machine learning models between organizations created rapid developments between federated learning and data engineering practices. The research investigates a new framework which combines trust mechanisms with decentralization methods and engineering discipline for the development of explainable and secure scalable AI systems. Data sovereignty and integrity together with verifiable model training are achieved through decentralized data pipelines which operate without exposing raw dataset information. The developed framework delivers reliable data processing methods which optimize federalized system operations and features trust-building features involving differential privacy implementation and secure aggregation capabilities alongside blockchain auditing capabilities. Our work includes the presentation of engineering methodologies for monitoring and orchestration as well as fault-tolerance techniques when deploying FL models across multiple heterogonous platforms. Results from experimental benchmark tests establish advancements in model accuracy performance and reductions in communication demands and improvements in trustworthiness compared to standard centralized and naïve federated learning models. The data indicates that industries which need strong privacy and transparency requirements should adopt engineering-centered federated learning systems as the basis for trustworthy artificial intelligence frameworks.