  Federated Learning is a promising machine learning paradigm when multiple
parties collaborate to build a high-quality machine learning model.
Nonetheless, these parties are only willing to participate when given enough
incentives, such as a fair reward based on their contributions. Many studies
explored Shapley value based methods to evaluate each party's contribution to
the learned model. However, they commonly assume a semi-trusted server to train
the model and evaluate the data owners' model contributions, which lacks
transparency and may hinder the success of federated learning in practice. In
this work, we propose a blockchain-based federated learning framework and a
protocol to transparently evaluate each participant's contribution. Our
framework protects all parties' privacy in the model building phase and
transparently evaluates contributions based on the model updates. The
experiment with the handwritten digits dataset demonstrates that the proposed
method can effectively evaluate the contributions.
