Goldfish: An Efficient Federated Unlearning Framework

arXiv:2404.03180v1 Announce Type: cross
Abstract: With recent legislation on the right to be forgotten, machine unlearning has emerged as a crucial research area. It facilitates the removal of a user's data from federated trained machine learning models without the necessity for retraining from scratch. However, current machine unlearning algorithms are confronted with challenges of efficiency and validity.To address the above issues, we propose a new framework, named Goldfish. It comprises four modules: basic model, loss function, optimization, and extension. To …

algorithms area arxiv challenges cs.cr cs.lg current data federated framework legislation machine machine learning machine learning models research right to be forgotten