Can I read Federated Unlearning with Gradient Descent and Conflict Mitigation on EtoBox?

Federated Unlearning with Gradient Descent and Conflict Mitigation by Pan, Zibin; Wang, Zhichao; Li, Chi; Zheng, Kaiyan; Wang, Boqi; Tang, Xiaoying; Zhao, Junhua is a scholarly article available to read on EtoBox.

What is Federated Unlearning with Gradient Descent and Conflict Mitigation about?

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 util

Author

Pan, Zibin; Wang, Zhichao; Li, Chi; Zheng, Kaiyan; Wang, Boqi; Tang, Xiaoying; Zhao, Junhua

Published

2024

Language

EN