Survey on federated learning threats: Concepts, taxonomy on attacks and defences, experimental study and challenges.

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 the protection against adversarial attacks harder 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. Finally, we present our learned lessons and challenges. • We claim that adversarial attacks are a significant challenge in federated learning. • We propose taxonomies of adversarial attacks and defences in federated learning. • We conduct a wide experimental study the results of which support our claim. • We define guidelines for selecting a proper defence method according to the attack. • We stand out a set of lessons learnt, open challenges and final conclusions. [ABSTRACT FROM AUTHOR]