提出可验证联邦遗忘框架，解决数据删除不可信难题
Towards Verifiable Federated Unlearning: Framework, Challenges, and The Road Ahead
- 构建veriFUL框架，明确验证主体与目标
- 提出新指标，实现对数据影响移除的可信评估
- 适合医疗等高敏感领域，推动隐私合规落地
联邦遗忘（FUL）使分布式客户端训练的模型能够移除特定数据的影响，符合隐私法规中“被遗忘权”的要求。客户通过数据贡献获得隐私保护控制，服务方则利用去中心化计算和数据实时性获益。然而，当前缺乏可靠机制验证数据影响是否已被彻底清除，仅靠简单通知或度量无法提供充分信任保障。本文提出veriFUL参考框架，形式化定义验证实体、目标、方法与评估指标，整合现有成果并引入新见解与量化标准。文章进一步揭示研究挑战，展望潜在应用场景与发展路径，旨在为研究人员和实践者提供全面资源，推动可验证联邦遗忘的发展。
原文摘要 · Abstract (English)
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.
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