As Machine Learning approaches get more sophisticated, many applications of machine learning in the healthcare sector are proposed (Mathur, 2019). However, medical data is often distributed across multiple institutions (i.e., hospitals, insurance providers etc.) and cannot be aggregated due to privacy concerns and ownership structures (Brisimi et al., 2018). Federated Learning (FL) is a technique that can be employed to enable distributed Machine Learning without sharing the underlying data (Kairouz et al., 2019; McMahan et al., 2016). There have been many approaches implementing federated learning in conjunction with blockchain technology. This avoids the single point of failure of centralised federated learning and promotes decentralisation. However, there are many competing methods and approaches on how to integrate the blockchain layer into a FL scheme. This thesis gives an overview on existing approaches, determines important requirements on the blockchain layer, then compares existing blockchain solutions and evaluates promising candidates using practical experiments. A systematic literature review on the use of FL with blockchain technology was conducted, after filtering, 49 approaches examined and categorised according to their mode of integrating the blockchain into a federated learning system. The analysis of requirements for the blockchain layer pointed towards a private permissioned blockchain with emphasis on security, reliability and flexibility. Afterwards, different blockchain solutions found in literature were compared. The blockchain solutions Ethereum and Hyperledger Fabric were further analysed using experiments. The analytic and experimental results suggest that the use of the blockchain solution Hyperledger Fabric is well-suited for facilitating a federated learning system in a healthcare setting.