论文标题
联合的未学习
Federated Unlearning
论文作者
论文摘要
联合学习（FL）最近成为有前途的分布式机器学习（ML）范式。 “被遗忘的权利”和反对数据中毒攻击的实际需求要求有效的技术，这些技术可以从训练有素的FL模型中删除或取消学习特定的培训数据。但是，在ML的背景下，现有的未学习技术不再实际上是由于FL和ML从数据中学习的方式固有的区别。因此，如何从FL模型中启用有效的数据删除，在很大程度上仍然不足。 In this paper, we take the first step to fill this gap by presenting FedEraser, the first federated unlearning methodology that can eliminate the influence of a federated client's data on the global FL model while significantly reducing the time used for constructing the unlearned FL model.The basic idea of FedEraser is to trade the central server's storage for unlearned model's construction time, where FedEraser reconstructs the unlearned model by leveraging the historical parameter在FL培训过程中，已保留在中央服务器的联合客户的更新。进一步开发了一种新型的校准方法来校准保留的更新，该更新进一步用于迅速构建未经学习的模型，从而在维持模型疗效的同时，对未经学习模型的重建产生了重大加速。在四个现实数据集上的实验证明了Federaser的有效性，与从头开始的重新培训相比，预期的加速$ 4 \ times $。我们将我们的工作设想为FL的早期一步，以公平和透明的方式遵守法律和道德标准。
Federated learning (FL) has recently emerged as a promising distributed machine learning (ML) paradigm. Practical needs of the "right to be forgotten" and countering data poisoning attacks call for efficient techniques that can remove, or unlearn, specific training data from the trained FL model. Existing unlearning techniques in the context of ML, however, are no longer in effect for FL, mainly due to the inherent distinction in the way how FL and ML learn from data. Therefore, how to enable efficient data removal from FL models remains largely under-explored. In this paper, we take the first step to fill this gap by presenting FedEraser, the first federated unlearning methodology that can eliminate the influence of a federated client's data on the global FL model while significantly reducing the time used for constructing the unlearned FL model.The basic idea of FedEraser is to trade the central server's storage for unlearned model's construction time, where FedEraser reconstructs the unlearned model by leveraging the historical parameter updates of federated clients that have been retained at the central server during the training process of FL. A novel calibration method is further developed to calibrate the retained updates, which are further used to promptly construct the unlearned model, yielding a significant speed-up to the reconstruction of the unlearned model while maintaining the model efficacy. Experiments on four realistic datasets demonstrate the effectiveness of FedEraser, with an expected speed-up of $4\times$ compared with retraining from the scratch. We envision our work as an early step in FL towards compliance with legal and ethical criteria in a fair and transparent manner.
