Federated Learning with Blockchain-Based Security Mechanisms

Abstract

This paper proposes a novel framework for secure federated learning leveraging the inherent properties of blockchain technology. The core claim is that utilizing blockchain's distributed consensus mechanism can establish a robust and reliable system for federated learning, effectively safeguarding the privacy of participating entities' data. The proposed mechanism involves recording model updates on a blockchain, employing consensus to guarantee the authenticity and consistency of these updates, and integrating encryption techniques to protect data privacy. This approach addresses the existing security vulnerabilities within traditional federated learning systems, significantly enhancing their overall security posture. The system's architecture and operational principles are detailed, outlining a practical solution for building trustworthy and privacy-preserving federated learning environments. Further, the paper explores potential challenges and future research directions within this domain.