  The advent of Federated Learning (FL) highlights the practical necessity for
the 'right to be forgotten' for all clients, allowing them to request data
deletion from the machine learning model's service provider. This necessity has
spurred a growing demand for Federated Unlearning (FU). Feature unlearning has
gained considerable attention due to its applications in unlearning sensitive
features, backdoor features, and bias features. Existing methods employ the
influence function to achieve feature unlearning, which is impractical for FL
as it necessitates the participation of other clients in the unlearning
process. Furthermore, current research lacks an evaluation of the effectiveness
of feature unlearning. To address these limitations, we define feature
sensitivity in the evaluation of feature unlearning according to Lipschitz
continuity. This metric characterizes the rate of change or sensitivity of the
model output to perturbations in the input feature. We then propose an
effective federated feature unlearning framework called Ferrari, which
minimizes feature sensitivity. Extensive experimental results and theoretical
analysis demonstrate the effectiveness of Ferrari across various feature
unlearning scenarios, including sensitive, backdoor, and biased features.
