With the technological advances in machine learning, effective ways are
available to process the huge amount of data generated in real life. However,
issues of privacy and scalability will constrain the development of machine
learning. Federated learning (FL) can prevent privacy leakage by assigning
training tasks to multiple clients, thus separating the central server from the
local devices. However, FL still suffers from shortcomings such as
single-point-failure and malicious data. The emergence of blockchain provides a
secure and efficient solution for the deployment of FL. In this paper, we
conduct a comprehensive survey of the literature on blockchained FL (BCFL).
First, we investigate how blockchain can be applied to federal learning from
the perspective of system composition. Then, we analyze the concrete functions
of BCFL from the perspective of mechanism design and illustrate what problems
blockchain addresses specifically for FL. We also survey the applications of
BCFL in reality. Finally, we discuss some challenges and future research
directions.

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