遗忘的见证：高效的联邦去学习及其可见评估
Forgetting to Witness: Efficient Federated Unlearning and Its Visible Evaluation
AI总结：
本文提出首个完整联邦去学习流程，通过高效方法和可视化框架评估模型遗忘能力，无需存储历史数据。
摘要：
随着数据隐私和安全重要性的提升，联邦去学习作为新兴研究领域，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。本文提出首个完整的联邦去学习流程，包含联邦去学习方法和评估框架。我们提出的联邦去学习方法确保高效性和模型准确性，无需存储历史数据。它有效利用知识蒸馏模型结合各种优化机制。此外，我们提出名为Skyeye的框架，用于可视化联邦去学习模型的遗忘能力。该框架利用联邦去学习模型作为分类器，集成到生成对抗网络（GAN）中。随后，分类器和判别器指导生成器生成样本。在整个过程中，生成器从分类器的知识中学习。生成器通过样本生成可视化这一知识。最后，模型的遗忘能力基于删除数据与生成样本之间的相关性进行评估。全面的实验展示了所提联邦去学习方法和相应评估框架的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
