Survey on Blockchain-Enhanced Machine Learning

Abstract

The convergence of blockchain and Machine Learning (ML) promises to reshape technological innovation by enhancing security, efficiency, and transparency in ML systems. This survey explores the transformative potential of integrating these two technologies. We outline the foundational principles of blockchain and ML, clarifying their capabilities and synergies. We examine how blockchain strengthens ML as a secure, immutable platform for data sharing, model validation, and executing tasks. We emphasize the opportunities for heightened data security, improved model validation, and decentralized, privacypreserving systems. However, challenges exist like scalability, energy-wise, and the need for new tailored consensus mechanisms. We provide insights based on recent research at this intersection. Additionally, we explore emerging trends and future directions, like blockchain's application in federated learning for secure, transparent data sharing and model validation. We also investigate privacy-preserving systems such as Proof of Learning, where blockchain enables secure execution while maintaining data privacy. Moreover, we examine the potential for decentralized AI systems leveraging blockchain to deploy and execute models. This survey offers a comprehensive overview of the evolving landscape at the intersection of blockchain and ML, highlighting opportunities and challenges while suggesting future research directions.