VeriFU: verifiable federated unlearning

Jiang Zehao; Xiong Jinbo; Huang Jiayi; Zhang Yuanyuan; Tian Youliang

doi:10.11959/j.issn.2096-109x.AQ26024

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VeriFU: verifiable federated unlearning

Papers | 更新时间：2026-05-11

- VeriFU: verifiable federated unlearning
- Chinese Journal of Network and Information Security Vol. 12, Issue 2, Pages: 104-117(2026)
- 作者机构：
  
  1.福建师范大学计算机与网络空间安全学院，福建福州 350117
  2.福建师范大学福建省网络安全与密码技术重点实验室，福建福州 350117
  3.贵州大学大数据与信息工程学院，贵州贵阳 550025
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62272102;62272123);The Natural Science Foundation Project of Fujian Province(2023J02014;2024J08163)
- DOI：10.11959/j.issn.2096-109x.AQ26024
  CLC： TP181
- Received：29 January 2026，
  
  Revised：2026-04-03，
  
  Accepted：07 April 2026，
  
  Published：25 April 2026
- 稿件说明：
移动端阅览
江泽豪, 熊金波, 黄佳毅, 等. VeriFU：可验证的联邦遗忘学习[J]. 网络与信息安全学报, 2026, 12(2): 104-117.

Jiang Zehao, Xiong Jinbo, Huang Jiayi, et al. VeriFU: verifiable federated unlearning[J]. Chinese Journal of Network and Information Security, 2026, 12(2): 104-117.
江泽豪, 熊金波, 黄佳毅, 等. VeriFU：可验证的联邦遗忘学习[J]. 网络与信息安全学报, 2026, 12(2): 104-117. DOI： 10.11959/j.issn.2096-109x.AQ26024.

Jiang Zehao, Xiong Jinbo, Huang Jiayi, et al. VeriFU: verifiable federated unlearning[J]. Chinese Journal of Network and Information Security, 2026, 12(2): 104-117. DOI： 10.11959/j.issn.2096-109x.AQ26024.

摘要

联邦遗忘学习为客户端提供了遗忘贡献的支持，然而，客户端难以验证服务器是否诚实且有效地移除了其贡献。目前的大部分研究忽视了这一重要方面，通过所构建的基于零知识证明的验证模型，提出完整的可验证联邦遗忘学习框架，并结合动态更新的默克尔树结构，提出了一种可验证的联邦遗忘学习方案，方案具有零知识性，能够在保护其他客户端隐私数据不泄露的前提下高效生成服务器遗忘操作的密码学证明。通过实验评估了方案的有效性、计算开销，与基于RSA（Rivest-Shamir-Adleman）累加器和基于哈希链的方案相比，当模型参数量为

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4.23333359

2.53999996

数量级时，所提方案在证明生成速度上相较于RSA方案提升了约两个数量级，在验证速度上相较于RSA方案提升了13.2倍，且避免了哈希链方案验证开销随数据规模线性增长的瓶颈。

Abstract

Federated unlearning enables clients to withdraw their contributions from a global model.However

enabling clients to verify whether the server has honestly and effectively removed their contributions remains a critical challenge. To address this aspect

which has been largely overlooked in existing literature

a verification model based on zero-knowledge proofs was constructed

and a comprehensive framework for verifiable federated unlearning was proposed. Combined with a dynamically updated Merkle tree structure

a novel verifiable federated unlearning scheme was presented characterized by its zero-knowledge property. This allows for the efficient generation of cryptographic proofs for server unlearning operations while rigorously protecting the data privacy of other clients. We evaluate the effectiveness and computational overhead of the proposed scheme. Comparative experiments with Rivest-Shamir-Adleman (RSA) accumulator-based and Hash chain-based schemes demonstrate that

when the model parameter size reaches the order of

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4.23333359

2.53999996

the proposed scheme improves proof generation speed by approximately two orders of magnitude and verification speed by 13.2 times compared to the RSA-based scheme. Furthermore

it effectively avoids the scalability bottleneck of data linear growth in verification overhead inherent in Hash chain-based schemes.

关键词

Keywords

references

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