  Federated Unlearning (FU) is gaining prominence for its capability to
eliminate influences of Federated Learning (FL) users' data from trained global
FL models. A straightforward FU method involves removing the unlearned users
and subsequently retraining a new global FL model from scratch with all
remaining users, a process that leads to considerable overhead. To enhance
unlearning efficiency, a widely adopted strategy employs clustering, dividing
FL users into clusters, with each cluster maintaining its own FL model. The
final inference is then determined by aggregating the majority vote from the
inferences of these sub-models. This method confines unlearning processes to
individual clusters for removing a user, thereby enhancing unlearning
efficiency by eliminating the need for participation from all remaining users.
However, current clustering-based FU schemes mainly concentrate on refining
clustering to boost unlearning efficiency but overlook the potential
information leakage from FL users' gradients, a privacy concern that has been
extensively studied. Typically, integrating secure aggregation (SecAgg) schemes
within each cluster can facilitate a privacy-preserving FU. Nevertheless,
crafting a clustering methodology that seamlessly incorporates SecAgg schemes
is challenging, particularly in scenarios involving adversarial users and
dynamic users. In this connection, we systematically explore the integration of
SecAgg protocols within the most widely used federated unlearning scheme, which
is based on clustering, to establish a privacy-preserving FU framework, aimed
at ensuring privacy while effectively managing dynamic user participation.
Comprehensive theoretical assessments and experimental results show that our
proposed scheme achieves comparable unlearning effectiveness, alongside
offering improved privacy protection and resilience in the face of varying user
participation.
