Federated Feature Engineering: PrivacyPreserving ML-Driven Pipelines across Distributed Data Sources
Pulicharla Data Engineer Staff, Frederick, Maryland, USA, Mohan Raja · International Journal of Innovative Research in Science Engineering and Technology · 2025
As data privacy regulations grow increasingly stringent, organizations are seeking methods to collaborate on machine learning (ML) initiatives without compromising data confidentiality. Federated learning has emerged as a viable paradigm; however, its success largely hinges on the quality and consistency of features extracted across decentralized nodes. This paper proposes a novel framework called "Federated Feature Engineering (FFE)," which extends the principles of federated learning into the realm of feature transformation and selection. Our proposed architecture enables distributed feature engineering pipelines while preserving data locality, ensuring privacy compliance, and maintaining high model performance. FFE introduces a structured, multi-layered methodology that allows participating nodes to independently engineer features from local data, while contributing to a shared understanding of data semantics and importance without sharing sensitive raw data. By leveraging privacy-preserving techniques such as secure multi-party computation, differential privacy, and homomorphic encryption, FFE ensures that valuable insights can be shared across nodes in a secure and compliant manner. The architecture also includes mechanisms for federated imputation, encoding, normalization, and feature selection based on statistical and model-driven evaluations, such as federated SHAP scoring and mutual information analysis. In contrast to conventional federated learning, which focuses solely on model training, FFE addresses the pre-modeling phase—where much of the predictive power is determined. Furthermore, this framework facilitates interoperability between heterogeneous data environments and offers a modular foundation for building cross-domain AI systems. Experimental results demonstrate that FFE achieves near-centralized performance while maintaining complete privacy-preserving properties, making it a significant milestone toward democratized, secure, and efficient AI development.