Integrating Federated Learning and Blockchain for Secure and Scalable Data Lakehouse Architectures

Abstract

The rapid growth of distributed data ecosystems has necessitated the development of secure, scalable, and privacy-preserving data management architectures. Traditional centralized data lake systems face significant challenges, including data privacy risks, lack of trust, and vulnerability to cyber threats. This study proposes an integrated framework that combines federated learning (FL) and blockchain technology within data Lakehouse architecture to address these limitations. The proposed system enables decentralized model training while ensuring data integrity, transparency, and scalability. The architecture consists of four primary layers: distributed data sources, federated learning clients, blockchain validation, and a centralized lake house for storage and analytics. Federated learning allows data to remain locally stored, reducing privacy risks, while blockchain introduces a trust layer that validates model updates using consensus mechanisms. The analysis of system performance demonstrates that the integration of blockchain improves trust and security but introduces additional latency compared to traditional and standalone federated systems. However, this trade-off is justified by enhanced protection against model poisoning and unauthorized data manipulation. Furthermore, the study evaluates the balance between privacy preservation and model performance, highlighting that increased security mechanisms may slightly reduce model accuracy but significantly improve data protection. The proposed framework also enhances traceability and auditability, enabling better governance and compliance in multi-stakeholder environments.