Federated Unlearning

Federated unlearning addresses the challenge of removing a participant's data from a collaboratively trained machine learning model in federated learning settings, respecting data privacy and the "right to be forgotten." Current research focuses on developing efficient algorithms that avoid costly retraining, employing techniques like contribution dampening, knowledge distillation, and model explanation to selectively remove a client's influence. These advancements are crucial for ensuring data privacy in federated learning applications across various domains, particularly in sensitive areas like healthcare and finance, where data protection is paramount.