Abstract
Federated Learning (FL) has received much attention in recent years. However, although clients are not required to share their data in FL, the global model itself can implicitly remember clients' local data. Therefore, it's necessary to effectively remove the target client's data from the FL global model to ease the risk of privacy leakage and implement ``the right to be forgotten". Federated Unlearning (FU) has been considered a promising way to remove data without full retraining. But the model utility easily suffers significant reduction during unlearning due to the gradient conflicts. Furthermore, when conducting the post-training to recover the model utility, the model is prone to move back and revert what has already been unlearned. To address these issues, we propose Federated Unlearning with Orthogonal Steepest Descent (FedOSD). We first design an unlearning Cross-Entropy loss to overcome the convergence issue of the gradient ascent. A steepest descent direction for unlearning is then calculated in the condition of being non-conflicting with other clients' gradients and closest to the target client's gradient. This benefits to efficiently unlearn and mitigate the model utility reduction. After unlearning, we recover the model utility by maintaining the achievement of unlearning. Finally, extensive experiments in several FL scenarios verify that FedOSD outperforms the SOTA FU algorithms in terms of unlearning and model utility.
Abstract (translated)
近年来,联邦学习(FL)受到了广泛关注。然而,尽管在联邦学习中客户端不需要共享他们的数据,但全局模型自身可能隐式地记住各个客户端的本地数据。因此,为了减轻隐私泄露的风险并实施“被遗忘权”,有效从联邦学习的全局模型中移除目标客户端的数据是必要的。联邦撤销(FU)被认为是一种无需完全重新训练即可删除数据的有前途的方法。但是,在进行撤销时,由于梯度冲突,模型效用往往会显著下降。此外,在后续训练以恢复模型效用时,模型容易回退并逆转已撤销的内容。 为了解决这些问题,我们提出了基于正交最速下降法的联邦撤销(FedOSD)。首先设计了一种用于克服梯度上升收敛问题的撤销交叉熵损失函数。接着计算了一个在不与其他客户端梯度冲突且尽可能接近目标客户端梯度的情况下进行撤销的最佳方向。这种方法有助于高效地进行数据删除,同时最大限度减少模型效用的下降。在完成撤销后,我们通过保持已删除内容的效果来恢复模型的效用。 最后,我们在多个联邦学习场景中进行了广泛的实验,并验证了FedOSD在撤销和模型效用方面优于现有最佳的FU算法。
URL
https://arxiv.org/abs/2412.20200