SoK: Challenges and Opportunities in Federated Unlearning
Hyejun Jeong Shiqing Ma Amir Houmansadr
Federated learning (FL), introduced in 2017, facilitates collaborative
learning between non-trusting parties with no need for the parties to
explicitly share their data among themselves. This allows training models on
user data while respecting privacy regulations such as GDPR and CPRA. However,
emerging privacy requirements may mandate model owners to be able to
\emph{forget} some learned data, e.g., when requested by data owners or law
enforcement. This has given birth to an active field of research called
\emph{machine unlearning}. In the context of FL, many techniques developed for
unlearning in centralized settings are not trivially applicable! This is due to
the unique differences between centralized and distributed learning, in
particular, interactivity, stochasticity, heterogeneity, and limited
accessibility in FL. In response, a recent line of work has focused on
developing unlearning mechanisms tailored to FL.
  This SoK paper aims to take a deep look at the \emph{federated unlearning}
literature, with the goal of identifying research trends and challenges in this
emerging field. By carefully categorizing papers published on FL unlearning
(since 2020), we aim to pinpoint the unique complexities of federated
unlearning, highlighting limitations on directly applying centralized
unlearning methods. We compare existing federated unlearning methods regarding
influence removal and performance recovery, compare their threat models and
assumptions, and discuss their implications and limitations. For instance, we
analyze the experimental setup of FL unlearning studies from various
perspectives, including data heterogeneity and its simulation, the datasets
used for demonstration, and evaluation metrics. Our work aims to offer insights
and suggestions for future research on federated unlearning.