The growing need to analyze distributed sensitive data while preserving privacy has led to the emergence of Federated Learning (FL), a paradigm enabling collaborative model training without centralizing data. However, traditional FL architectures face challenges including single points of failure, potential privacy leaks through model updates, and vulnerabilities to malicious participants. This chapter explores how blockchain technology can address these challenges, presenting a comprehensive analysis of privacy-preserving blockchain-based federated learning systems. We examine various architectural approaches, from decoupled systems where blockchain and training nodes are separate, to coupled systems with dynamic role assignment. We provide a taxonomy of privacy-preserving mechanisms, including differential privacy and cryptographic techniques, analyzing their integration with blockchain-based FL. We discuss both theoretical foundations and practical implementations, highlighting how different approaches balance privacy guarantees with system performance.