Unlearning Algorithm

Machine unlearning aims to remove the influence of specific training data from a pre-trained model, addressing privacy and data protection concerns. Current research focuses on developing and evaluating algorithms for various model architectures, including language models and those used in medical imaging, with a particular emphasis on mitigating privacy leakage and preserving model utility after data removal. Challenges remain in achieving complete data removal, ensuring fairness across different data subsets, and maintaining model performance, highlighting the need for more robust and efficient unlearning techniques with reliable evaluation methods.