  Federated Learning (FL) enables collaborative training of a Machine Learning
(ML) model across multiple parties, facilitating the preservation of users' and
institutions' privacy by keeping data stored locally. Instead of centralizing
raw data, FL exchanges locally refined model parameters to build a global model
incrementally. While FL is more compliant with emerging regulations such as the
European General Data Protection Regulation (GDPR), ensuring the right to be
forgotten in this context - allowing FL participants to remove their data
contributions from the learned model - remains unclear. In addition, it is
recognized that malicious clients may inject backdoors into the global model
through updates, e.g. to generate mispredictions on specially crafted data
examples. Consequently, there is the need for mechanisms that can guarantee
individuals the possibility to remove their data and erase malicious
contributions even after aggregation, without compromising the already acquired
"good" knowledge. This highlights the necessity for novel Federated Unlearning
(FU) algorithms, which can efficiently remove specific clients' contributions
without full model retraining. This survey provides background concepts,
empirical evidence, and practical guidelines to design/implement efficient FU
schemes. Our study includes a detailed analysis of the metrics for evaluating
unlearning in FL and presents an in-depth literature review categorizing
state-of-the-art FU contributions under a novel taxonomy. Finally, we outline
the most relevant and still open technical challenges, by identifying the most
promising research directions in the field.
