Towards Federated Domain Unlearning: Verification Methodologies and Challenges

TL;DR

This paper investigates the challenges of unlearning specific domain data in federated learning, highlighting the limitations of current methods and proposing new evaluation strategies for effective domain-specific data removal.

Contribution

It provides the first empirical analysis of federated domain unlearning, identifies key challenges, and introduces tailored evaluation methodologies for domain-specific data erasure.

Findings

Current unlearning methods often degrade model performance in non-targeted domains.

Unlearning impacts deeper layers, erasing critical representations.

Proposed evaluation methods improve assessment of domain-specific unlearning.

Abstract

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.