迈向联邦领域遗忘：验证方法与挑战
Towards Federated Domain Unlearning: Verification Methodologies and Challenges
AI总结：
本文针对联邦学习中多领域场景下的“被遗忘权”挑战，首次全面实证研究了联邦领域遗忘技术，发现现有方法会损害模型深层表征，并提出了专为该场景设计的新型评估方法以准确验证特定领域数据的擦除。
摘要：
联邦学习（FL）已发展成为一种强大的工具，用于跨多个实体进行协作模型训练，确保医疗和金融等敏感领域的数据隐私。然而，“被遗忘权”（RTBF）的引入带来了新的挑战，需要通过联邦遗忘在不进行完整模型重训的情况下删除数据。传统的联邦学习遗忘方法最初并未考虑领域特异性，无法充分应对多领域场景的复杂性，常常影响非目标领域模型的准确性，或导致跨所有领域的均匀遗忘。我们的工作提出了关于联邦领域遗忘的首次全面实证研究，分析了多领域背景下现有技术的特征与挑战。我们发现这些方法会失效，特别是因为它们忽视了特定领域数据的细微影响，这可能导致显著的性能下降和不准确的模型行为。我们的发现揭示，遗忘操作会对模型的较深层产生不成比例的影响，抹去了在早期训练阶段获得的关键表征子空间。为此，我们提出了专为联邦领域遗忘量身定制的新型评估方法，旨在准确评估和验证特定领域数据的擦除，同时不损害模型的整体完整性和性能。这项研究不仅突显了在联邦学习中以领域为中心的遗忘策略的迫切需求，也为有效评估和实施这些技术树立了新的先例。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
