Blockchain-Enabled Federated Learning

TL;DR

This paper analyzes blockchain-enabled federated learning systems, exploring their architecture, consensus mechanisms, storage solutions, and practical deployment, demonstrating their effectiveness in secure, transparent, and scalable collaborative AI.

Contribution

It provides a comprehensive taxonomy and analysis of BCFL architectures, introduces novel consensus mechanisms tailored for federated learning, and presents a practical case study validating real-world deployment.

Findings

BCFL systems can match centralized performance in collaborative tasks.

Blockchain enhances security and trust in federated learning environments.

Practical deployment shows BCFL's viability across various industries.

Abstract

Blockchain-enabled federated learning (BCFL) addresses fundamental challenges of trust, privacy, and coordination in collaborative AI systems. This chapter provides comprehensive architectural analysis of BCFL systems through a systematic four-dimensional taxonomy examining coordination structures, consensus mechanisms, storage architectures, and trust models. We analyze design patterns from blockchain-verified centralized coordination to fully decentralized peer-to-peer networks, evaluating trade-offs in scalability, security, and performance. Through detailed examination of consensus mechanisms designed for federated learning contexts, including Proof of Quality and Proof of Federated Learning, we demonstrate how computational work can be repurposed from arbitrary cryptographic puzzles to productive machine learning tasks. The chapter addresses critical storage challenges by examining multi-tier architectures that balance blockchain's transaction constraints with neural networks' large parameter requirements while maintaining cryptographic integrity. A technical case study of the TrustMesh framework illustrates practical implementation considerations in BCFL systems through distributed image classification training, demonstrating effective collaborative learning across IoT devices with highly non-IID data distributions while maintaining complete transparency and fault tolerance. Analysis of real-world deployments across healthcare consortiums, financial services, and IoT security applications validates the practical viability of BCFL systems, achieving performance comparable to centralized approaches while providing enhanced security guarantees and enabling new models of trustless collaborative intelligence.