Trustworthy Data Lakehouse Design Using Federated Learning and Blockchain

Abstract

The increasing amount of heterogeneous enterprise data has catalysed an expedient requirement of confiding, scalable, and privacy-preserving analytics structures. When data is distributed among various stakeholders, traditional data lake houses are prone to data integrity, provenance, and governance problems as well as secure model training. This paper seeks to overcome these difficulties by introducing a Trustworthy Data Lakehouse Architecture which combines Federated Learning (FL) with Blockchain-enabled governance to enhance safe, auditable and regulation compliant data analytics. The framework designed includes a built-in metadata layer, decentralized model-training pipeline, immutable ledger, based on blockchain and data provenance, and the privacy protection mechanisms of differential-privacy. Multi-organization collaboration without raw data exchange is possible thanks to Federated Learning, and end-to-end trust is ensured by blockchain which supports consensus-based validation, lineage tracking based on tamper-proof, and access control with smart-contracts. Experimental analysis is used to show that there are data reliability, model accuracy, latency, and confidentiality improvements over traditional centralized lake houses. The presented solution opens up a strong base of constructing transparent, secure and scaled out data ecosystems applicable to finance, healthcare, supply chain among other sensitive areas.