  Across industries, there is an ever-increasing rate of data sharing for
collaboration and innovation between organizations and their customers,
partners, suppliers, and internal teams. However, many enterprises are
restricted from freely sharing data due to regulatory restrictions across
different regions, performance issues in moving large volume data, or
requirements to maintain autonomy. In such situations, the enterprise can
benefit from the concept of federated learning, in which machine learning
models are constructed at various geographic sites. In this paper, we introduce
a general framework, namely BDSP, to share data among enterprises based on
Blockchain and federated learning techniques. Specifically, we propose a
transparency contribution accounting mechanism to estimate the valuation of
data and implement a proof-of-concept for further evaluation. The extensive
experimental results show that the proposed BDSP has a competitive performance
with higher training accuracy, an increase of over 5%, and lower communication
overhead, reducing 3 times, compared to baseline approaches.
