Federated learning is a key methodology for decentralized training in machine learning under the paradigm of privacy preservation. However, many debilitating issues threaten its viability, primarily concerning model accountability and transparency, difficulties in making the model explainable and reliable. In this chapter, we focus on analyzing advances made in blockchain-aided federated learning systems to deal with these contrasting issues. Because of its immutable and decentralized nature, blockchain technology is touted as providing transparency and accountability to federated learning systems. The blockchain enables the recording and confirmation of model updates, decision-making processes, and causal links between participants, resulting in the provision of a tamper-proof audit trail. This ability allows stakeholders to monitor model behavior, verify contributions, and identify biases or adversarial activities. The blockchain-federated learning partnership will help researchers build more ethical, transparent, and scalable artificial intelligence systems. This chapter assesses the advances made so far, including a brief introduction to federated learning, an outline of state-of-the-art innovations in the blockchain-based federated learning systems, an analysis of key issues faced by federated learning and blockchain-enhanced federated learning, and possibilities of further enhancement in reliability and accountability. The goal of this chapter is to further the ongoing efforts aimed at addressing the technological and ethical issues pervading the era of distributed intelligence.