  Federated learning is a decentralized machine learning paradigm that allows
multiple clients to collaborate by leveraging local computational power and the
models transmission. This method reduces the costs and privacy concerns
associated with centralized machine learning methods while ensuring data
privacy by distributing training data across heterogeneous devices. On the
other hand, federated learning has the drawback of data leakage due to the lack
of privacy-preserving mechanisms employed during storage, transfer, and
sharing, thus posing significant risks to data owners and suppliers. Blockchain
technology has emerged as a promising technology for offering secure
data-sharing platforms in federated learning, especially in Industrial Internet
of Things (IIoT) settings. This survey aims to compare the performance and
security of various data privacy mechanisms adopted in blockchain-based
federated learning architectures. We conduct a systematic review of existing
literature on secure data-sharing platforms for federated learning provided by
blockchain technology, providing an in-depth overview of blockchain-based
federated learning, its essential components, and discussing its principles,
and potential applications. The primary contribution of this survey paper is to
identify critical research questions and propose potential directions for
future research in blockchain-based federated learning.
