BLOCKCHAIN-ENABLED FEDERATED DATA SCIENCE FOR PRIVACY-PRESERVING MULTI-ORGANIZATION ANALYTICS

Federated learning enables collaborative model training across multiple organizations without sharing raw data, addressing growing privacy and regulatory constraints. However, existing approaches often rely on centralized coordination and lack transparent governance and accountability. Blockchain technology enhances federated analytics by providing decentralized trust, immutable logging, and automated policy enforcement. This paper presents a comprehensive survey of blockchain-enabled federated data science frameworks for privacy-preserving multi-organization analytics. The study reviews privacy-aware data collection and preprocessing, secure federated model development, and blockchain-based governance and provenance mechanisms. Architectural designs, system trade-offs, and research challenges related to scalability, interoperability, adversarial environments, and regulatory compliance are analyzed. The paper proposes an integrated framework combining privacy-preserving data processing, federated learning, and decentralized governance to improve trust, reliability, and data integrity in collaborative analytics ecosystems. Keywords: Blockchain-based federated learning; Data governance; Middleware integration; Encode compilation; Federated identity management.