Abstract
Deep learning has gained huge traction in recent years because of its
potential to make informed decisions. A large portion of today’s deep
learning systems are based on centralized servers and fall short in
providing operational transparency, traceability, reliability, security,
and trusted data provenance features. Also, training deep learning
models by utilizing centralized data is vulnerable to the single point
of failure problem. In this paper, we explore the importance of
integrating blockchain technology with deep learning. We review the
existing literature focused on the integration of blockchain with deep
learning. We classify and categorize the literature by devising a
thematic taxonomy based on seven parameters; namely, blockchain type,
deep learning models, deep learning specific consensus protocols,
application area, services, data types, and deployment goals. We provide
insightful discussions on the state-of-the-art blockchain-based deep
learning frameworks by highlighting their strengths and weaknesses.
Furthermore, we compare the existing blockchain-based deep learning
frameworks based on four parameters such as blockchain type, consensus
protocol, deep learning method, and dataset. Finally, we present
important research challenges which need to be addressed to develop
highly efficient, robust, and secure deep learning frameworks.