随时遗忘任意数据：垂直联邦学习的理论认证遗忘框架
Forgetting Any Data at Any Time: A Theoretically Certified Unlearning Framework for Vertical Federated Learning
AI总结：
针对垂直联邦学习缺乏合规遗忘机制的问题，提出首个具有理论保证的模型与数据无关、支持异步遗忘的框架，可随时移除任意数据，满足被遗忘权要求。
摘要：
机器学习中的隐私问题因GDPR等法规而加剧，这些法规强制执行“被遗忘权”（RTBF），推动了机器遗忘作为关键研究领域的出现。垂直联邦学习（VFL）通过聚合分布在多方之间的样本特征来实现协作模型训练，同时保留各数据源的隐私。这种范式在医疗、金融及其他隐私敏感领域已被广泛采用。然而，现有的VFL系统缺乏满足RTBF要求的稳健机制，因为针对VFL的遗忘方法仍未得到充分探索。在这项工作中，我们引入了第一个具有理论保证遗忘能力的VFL框架，使得能够随时移除任何数据。与先前的方法（这些方法对模型架构或数据类型施加了限制性假设以进行移除）不同，我们的解决方案是模型无关和数据无关的，提供通用兼容性。此外，我们的框架支持异步遗忘，消除了在遗忘过程中所有方需要同时在线的要求。这些进展解决了当前VFL系统中的关键差距，确保符合RTBF，同时保持操作灵活性。我们公开所有实现，网址为https://github.com/wangln19/vertical-federated-unlearning。
英文摘要：
Privacy concerns in machine learning are heightened by regulations such as the GDPR, which enforces the "right to be forgotten" (RTBF), driving the emergence of machine unlearning as a critical research field. Vertical Federated Learning (VFL) enables collaborative model training by aggregating a sample's features across distributed parties while preserving data privacy at each source. This paradigm has seen widespread adoption in healthcare, finance, and other privacy-sensitive domains. However, existing VFL systems lack robust mechanisms to comply with RTBF requirements, as unlearning methodologies for VFL remain underexplored. In this work, we introduce the first VFL framework with theoretically guaranteed unlearning capabilities, enabling the removal of any data at any time. Unlike prior approaches -- which impose restrictive assumptions on model architectures or data types for removal -- our solution is model- and data-agnostic, offering universal compatibility. Moreover, our framework supports asynchronous unlearning, eliminating the need for all parties to be simultaneously online during the forgetting process. These advancements address critical gaps in current VFL systems, ensuring compliance with RTBF while maintaining operational flexibility.We make all our implementations publicly available at https://github.com/wangln19/vertical-federated-unlearning.
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。
