With the increasing importance of data privacy and security, federated unlearning has emerged as a novel research field dedicated to ensuring that federated learning models no longer retain or leak relevant information once specific data has been deleted. In this paper, to the best of our knowledge, we propose the first complete pipeline for federated unlearning, which includes a federated unlearning approach and an evaluation framework. Our proposed federated unlearning approach ensures high efficiency and model accuracy without the need to store historical data.It effectively leverages the knowledge distillation model alongside various optimization mechanisms. Moreover, we propose a framework named Skyeye to visualize the forgetting capacity of federated unlearning models. It utilizes the federated unlearning model as the classifier integrated into a Generative Adversarial Network (GAN). Afterward, both the classifier and discriminator guide the generator in generating samples. Throughout this process, the generator learns from the classifier's knowledge. The generator then visualizes this knowledge through sample generation. Finally, the model's forgetting capability is evaluated based on the relevance between the deleted data and the generated samples. Comprehensive experiments are conducted to illustrate the effectiveness of the proposed federated unlearning approach and the corresponding evaluation framework.

翻译：随着数据隐私与安全的重要性日益提升，联邦遗忘已成为一个新兴研究领域，致力于确保联邦学习模型在特定数据被删除后不再保留或泄露相关信息。本文中，据我们所知，我们提出了首个完整的联邦遗忘流水线，包括一种联邦遗忘方法和一个评估框架。我们提出的联邦遗忘方法无需存储历史数据即可确保高效性和模型准确性，并有效结合了知识蒸馏模型与多种优化机制。此外，我们提出了一个名为Skyeye的框架，用于可视化联邦遗忘模型的遗忘能力。该框架将联邦遗忘模型作为分类器集成到生成对抗网络（GAN）中。随后，分类器和判别器共同引导生成器生成样本。在此过程中，生成器从分类器的知识中学习，并通过样本生成实现知识的可视化。最后，基于删除数据与生成样本之间的相关性评估模型的遗忘能力。通过全面的实验验证了所提出的联邦遗忘方法及对应评估框架的有效性。