Streamlined Federated Unlearning: Unite as One to Be Highly Efficient

Understand

Recently, the enactment of ``right to be forgotten" laws and regulations has imposed new privacy requirements on federated learning (FL).

• Researchers aim to remove the influence of certain data from the trained model without training from scratch through federated unlearning (FU).
• While current FU research has shown progress in enhancing unlearning efficiency, it often results in degraded model performance upon achieving the goal of data unlearning, necessitating additional steps to recover the performance of the unlearned model.
• Moreover, these approaches also suffer from many shortcomings such as high consumption of computational and storage resources.