  Federated Learning (FL) has evolved as a powerful tool for collaborative
model training across multiple entities, ensuring data privacy in sensitive
sectors such as healthcare and finance. However, the introduction of the Right
to Be Forgotten (RTBF) poses new challenges, necessitating federated unlearning
to delete data without full model retraining. Traditional FL unlearning
methods, not originally designed with domain specificity in mind, inadequately
address the complexities of multi-domain scenarios, often affecting the
accuracy of models in non-targeted domains or leading to uniform forgetting
across all domains. Our work presents the first comprehensive empirical study
on Federated Domain Unlearning, analyzing the characteristics and challenges of
current techniques in multi-domain contexts. We uncover that these methods
falter, particularly because they neglect the nuanced influences of
domain-specific data, which can lead to significant performance degradation and
inaccurate model behavior. Our findings reveal that unlearning
disproportionately affects the model's deeper layers, erasing critical
representational subspaces acquired during earlier training phases. In
response, we propose novel evaluation methodologies tailored for Federated
Domain Unlearning, aiming to accurately assess and verify domain-specific data
erasure without compromising the model's overall integrity and performance.
This investigation not only highlights the urgent need for domain-centric
unlearning strategies in FL but also sets a new precedent for evaluating and
implementing these techniques effectively.
