A Revocation Key-based Approach Towards Efficient Federated Unlearning
Rui-Zhen Xu, Sheng-Yi Hong, Po-Wen Chi, Ming-Hung Wang · 2023
Federated learning is an approach that ensures privacy in machine learning, but it has its limitations when it comes to preserving the right to be forgotten. To address this challenge, we propose a new method called Unlearning Key Revocation List (UKRL) for implementing federated unlearning. Our approach does not require clients’ data or models to be unlearned; instead, we use revocation keys to remove clients from the model. We pre-trained the model to recognize these keys, so the model will forget the revoked clients when their revocation keys are applied. We conducted four experiments using MNIST datasets to verify the effectiveness of our approach, and the results showed that our work is not only effective but also time-saving since the unlearning time is 0. In conclusion, we provide a new perspective on achieving federated unlearning.