Federated unlearning (FUL) enables removing the data influence from the model trained across distributed clients, upholding the right to be forgotten as mandated by privacy regulations. FUL facilitates a value exchange where clients gain privacy-preserving control over their data contributions, while service providers leverage decentralized computing and data freshness. However, this entire proposition is undermined because clients have no reliable way to verify that their data influence has been provably removed, as current metrics and simple notifications offer insufficient assurance. We envision unlearning verification becoming a pivotal and trust-by-design part of the FUL life-cycle development, essential for highly regulated and data-sensitive services and applications like healthcare. This article introduces veriFUL, a reference framework for verifiable FUL that formalizes verification entities, goals, approaches, and metrics. Specifically, we consolidate existing efforts and contribute new insights, concepts, and metrics to this domain. Finally, we highlight research challenges and identify potential applications and developments for verifiable FUL and veriFUL. This article aims to provide a comprehensive resource for researchers and practitioners to navigate and advance the field of verifiable FUL.

翻译：联邦遗忘学习（FUL）能够从分布式客户端训练的模型中移除数据影响，从而维护隐私法规所要求的被遗忘权。FUL促进了一种价值交换：客户端得以对其数据贡献实施隐私保护控制，而服务提供商则能利用去中心化计算与数据新鲜度。然而，由于当前指标与简单通知无法提供充分保证，客户端缺乏可靠方法来验证其数据影响已被可证明地移除，这使得整个方案的有效性受到质疑。我们设想将遗忘验证发展为FUL生命周期开发中关键且内嵌可信设计的组成部分，这对于医疗等高度监管与数据敏感的服务与应用至关重要。本文介绍了veriFUL——一个用于可验证FUL的参考框架，该框架形式化了验证实体、目标、方法与度量指标。具体而言，我们整合了现有研究成果，并为该领域贡献了新的见解、概念与度量标准。最后，我们重点阐述了研究挑战，并指出了可验证FUL及veriFUL的潜在应用与发展方向。本文旨在为研究人员与实践者提供全面资源，以引领并推动可验证FUL领域的发展。