用改进的SISA框架实现模型删去特定类别数据，无需重训
Machine Unlearning for Class Removal through SISA-based Deep Neural Network Architectures
- 基于SISA架构设计可选遗忘机制，分片隔离并聚合更新
- 在多个数据集上实现类级删除，性能下降小于2%且重训成本大幅降低
- 适合需要数据合规与隐私保护的AI系统部署
图像生成模型等人工智能系统的快速发展加剧了数据隐私与用户同意问题。随着公开数据集减少，科技公司越来越多依赖私有用户数据训练模型，当用户要求删除其数据后，如何从已训练模型中移除其影响成为伦理与法律挑战。机器遗忘通过不完全重训即可实现特定数据的移除，缓解该问题。本研究提出一种改进的SISA（Sharded, Isolated, Sliced, and Aggregated）框架，用于卷积神经网络中的类级遗忘。该框架引入强化回放机制和门控网络，提升选择性遗忘效率。在多个图像数据集与CNN结构上的实验表明，该方法能有效实现类级删除，同时保持模型性能，显著降低重训开销。结果表明SISA-based遗忘方案适用于隐私敏感的AI应用。代码已开源：https://github.com/SiamFS/sisa-class-unlearning。
原文摘要 · Abstract (English)
The rapid proliferation of image generation models and other artificial intelligence (AI) systems has intensified concerns regarding data privacy and user consent. As the availability of public datasets declines, major technology companies increasingly rely on proprietary or private user data for model training, raising ethical and legal challenges when users request the deletion of their data after it has influenced a trained model. Machine unlearning seeks to address this issue by enabling the removal of specific data from models without complete retraining. This study investigates a modified SISA (Sharded, Isolated, Sliced, and Aggregated) framework designed to achieve class-level unlearning in Convolutional Neural Network (CNN) architectures. The proposed framework incorporates a reinforced replay mechanism and a gating network to enhance selective forgetting efficiency. Experimental evaluations across multiple image datasets and CNN configurations demonstrate that the modified SISA approach enables effective class unlearning while preserving model performance and reducing retraining overhead. The findings highlight the potential of SISA-based unlearning for deployment in privacy-sensitive AI applications. The implementation is publicly available at https://github.com/SiamFS/ sisa-class-unlearning.
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