Secure Data Pipelines for Federated Learning in Regulated Environments

Author(s)	Sougandhika Tera
Country	United States
Abstract	This paper addresses the technical and regulatory challenges of building secure data pipelines to support federated learning (FL), where models train collaboratively across multiple organizations without sharing raw data. The paper explores privacy-preserving data engineering techniques such as differential privacy, homomorphic encryption, and secure aggregation within ETL frameworks. It outlines an architecture for orchestrating decentralized dataflows that comply with GDPR, HIPAA, and other regulatory standards while enabling cross-institutional AI innovation. By integrating secure connectors, encrypted model updates, and audit logging, the proposed pipeline design ensures both data protection and analytic utility, providing a blueprint for responsible AI deployment in healthcare, finance, and government sectors.
Keywords	Federated Learning, Secure Data Pipelines, Differential Privacy, Homomorphic Encryption, GDPR, HIPAA, ETL, Regulatory Compliance, Privacy-Preserving AI, Cross-Silo Learning.
Field	Engineering
Published In	Volume 17, Issue 1, January-June 2026
Published On	2026-02-05
Cite This	Secure Data Pipelines for Federated Learning in Regulated Environments - Sougandhika Tera - IJAIDR Volume 17, Issue 1, January-June 2026. DOI 10.71097/IJAIDR.v17.i1.1755
DOI	https://doi.org/10.71097/IJAIDR.v17.i1.1755
Short DOI	https://doi.org/hbtjrb

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