Erasing memories: implementing client unlearning in medical image analysis

Federated learning (FL) has become a cornerstone in medical image analysis, addressing the dual challenges of data volume growth and privacy concerns in centralized systems. Alongside FL, federated unlearning (FU) has emerged, focusing on the removal of sensitive information from models to comply with privacy regulations and ethical considerations. This paper introduces an efficient unlearning process using gradient ascent, aimed at balancing the "right to be forgotten" with the integrity of the learning process. Our approach, evaluated through the lenses of under-unlearning and over-unlearning, promises enhanced scalability and effectiveness in privacy-preserving machine learning, particularly in sensitive applications like medical image analysis.