  The evolution of privacy-preserving Federated Learning (FL) has led to an
increasing demand for implementing the right to be forgotten. The
implementation of selective forgetting is particularly challenging in FL due to
its decentralized nature. This complexity has given rise to a new field,
Federated Unlearning (FU). FU emerges as a strategic solution to address the
increasing need for data privacy, including the implementation of the `right to
be forgotten'. The primary challenge in developing FU approaches lies in
balancing the trade-offs in privacy, security, utility, and efficiency, as
these elements often have competing requirements. Achieving an optimal
equilibrium among these facets is crucial for maintaining the effectiveness and
usability of FL systems while adhering to privacy and security standards. This
survey provides a comprehensive analysis of existing FU methods, incorporating
a detailed review of the various evaluation metrics. Furthermore, we unify
these diverse methods and metrics into an experimental framework. Additionally,
the survey discusses potential future research directions in FU. Finally, a
continually updated repository of related open-source materials is available
at: https://github.com/abbottyanginchina/Awesome-Federated-Unlearning.
