This paper delves into the complexities of federated unlearning, highlighting the need for tailored mechanisms due to unique differences in distributed learning. The research aims to offer insights and recommendations for future studies on federated unlearning.
Federated unlearning presents unique challenges in the distributed learning environment, requiring tailored mechanisms for effective data removal while maintaining model performance.
Federated unlearning presents unique challenges in the distributed learning context, requiring tailored mechanisms for effective data removal while maintaining model performance.