Abstract:

With the rapid advancement of artificial intelligence, federated learning has been widely adopted in multiple sensitive domains such as healthcare and finance. However, amid growing demands for privacy protection, how to efficiently remove sensitive information from models while preserving data privacy remains a challenging task. To address this issue, this paper proposed a federated unlearning method that combines knowledge distillation with adversarial examples, aiming to balance model utility and privacy while minimizing the time overhead of the unlearning process. To validate the effectiveness of the proposed method, we designed two application scenarios and conducted comparative experiments using four datasets and six mainstream baseline methods. Experimental results demonstrate that the proposed method achieves a favorable trade-off among model usability, privacy, and runtime efficiency. On the CIFAR-10 and SVHN datasets, compared with the six baseline methods, our approach significantly improves privacy protection and runtime efficiency while maintaining model usability at a comparable level.

Key words: federated unlearning, machine unlearning, federated learning, adversarial examples, knowledge distillation