  Federated learning is an emerging privacy-preserving AI technique where
clients (i.e., organisations or devices) train models locally and formulate a
global model based on the local model updates without transferring local data
externally. However, federated learning systems struggle to achieve
trustworthiness and embody responsible AI principles. In particular, federated
learning systems face accountability and fairness challenges due to
multi-stakeholder involvement and heterogeneity in client data distribution. To
enhance the accountability and fairness of federated learning systems, we
present a blockchain-based trustworthy federated learning architecture. We
first design a smart contract-based data-model provenance registry to enable
accountability. Additionally, we propose a weighted fair data sampler algorithm
to enhance fairness in training data. We evaluate the proposed approach using a
COVID-19 X-ray detection use case. The evaluation results show that the
approach is feasible to enable accountability and improve fairness. The
proposed algorithm can achieve better performance than the default federated
learning setting in terms of the model's generalisation and accuracy.
