  Federated learning is a machine learning paradigm that emerges as a solution
to the privacy-preservation demands in artificial intelligence. As machine
learning, federated learning is threatened by adversarial attacks against the
integrity of the learning model and the privacy of data via a distributed
approach to tackle local and global learning. This weak point is exacerbated by
the inaccessibility of data in federated learning, which makes harder the
protection against adversarial attacks and evidences the need to furtherance
the research on defence methods to make federated learning a real solution for
safeguarding data privacy. In this paper, we present an extensive review of the
threats of federated learning, as well as as their corresponding
countermeasures, attacks versus defences. This survey provides a taxonomy of
adversarial attacks and a taxonomy of defence methods that depict a general
picture of this vulnerability of federated learning and how to overcome it.
Likewise, we expound guidelines for selecting the most adequate defence method
according to the category of the adversarial attack. Besides, we carry out an
extensive experimental study from which we draw further conclusions about the
behaviour of attacks and defences and the guidelines for selecting the most
adequate defence method according to the category of the adversarial attack.
This study is finished leading to meditated learned lessons and challenges.
