Federated Learning and Blockchain

Abstract

Federated learning (FL) has emerged as a transformative framework in artificial intelligence, addressing escalating concerns around data privacy, security, and distributed computation. Unlike conventional centralized learning systems that require data aggregation, FL enables collaborative model training across multiple clients while keeping data localized, thereby ensuring sovereignty and confidentiality. This chapter presents a comprehensive analysis of FL as a decentralized paradigm and explores its convergence with blockchain to establish secure, transparent, and tamper-resistant AI ecosystems. The discussion begins with FL fundamentals—its architecture, communication protocols, and key algorithms, such as Federated Averaging (FedAvg). Security and privacy mechanisms including differential privacy, homomorphic encryption, and secure multi-party computation are examined alongside defense strategies against adversarial and Byzantine threats. The chapter further highlights blockchain's role in strengthening FL's trust layer through immutable ledgers, smart contracts, and decentralized consensus, which mitigate single-point failures and enhance auditability. Practical applications span healthcare, finance, IoT, and edge computing, where federated–blockchain integration enables privacy-preserving analytics, fraud detection, and intelligent infrastructure management. Technical challenges—communication bottlenecks, heterogeneity, and scalability—are analyzed with solutions, such as model compression, adaptive client selection, and hierarchical coordination. Finally, the chapter discusses evaluation frameworks, ethical guidelines, and future directions involving blockchain, quantum computing, and transformer-based FL. Together, federated learning and blockchain redefine secure collaborative intelligence, paving the way for decentralized, trustworthy, and regulation-compliant AI systems.