  Federated learning (FL) is an emerging paradigm of collaborative machine
learning that preserves user privacy while building powerful models.
Nevertheless, due to the nature of open participation by self-interested
entities, it needs to guard against potential misbehaviours by legitimate FL
participants. FL verification techniques are promising solutions for this
problem. They have been shown to effectively enhance the reliability of FL
networks and help build trust among participants. Verifiable federated learning
has become an emerging topic of research that has attracted significant
interest from the academia and the industry alike. Currently, there is no
comprehensive survey on the field of verifiable federated learning, which is
interdisciplinary in nature and can be challenging for researchers to enter
into. In this paper, we bridge this gap by reviewing works focusing on
verifiable FL. We propose a novel taxonomy for verifiable FL covering both
centralised and decentralised FL settings, summarise the commonly adopted
performance evaluation approaches, and discuss promising directions towards a
versatile verifiable FL framework.
