Unlearning Framework

Machine unlearning aims to remove the influence of specific data points from trained machine learning models, addressing privacy concerns and promoting data control. Current research focuses on developing efficient unlearning algorithms for various model architectures, including deep neural networks, large language models, and graph neural networks, often employing techniques like gradient manipulation, parameter editing, and contrastive learning. This field is crucial for ensuring compliance with data privacy regulations and enhancing the trustworthiness and safety of deployed AI systems across diverse applications, from medical diagnosis to personalized recommendations. Challenges remain in balancing effective unlearning with the preservation of model utility and robustness against adversarial attacks.