SIFU introduces a novel method for efficient and provable client unlearning in federated optimization, addressing data removal challenges in machine learning models.
Goldfish is a novel framework for efficient and effective federated unlearning that enables the removal of a user's data from a trained machine learning model without the need for complete retraining.
A lightweight machine unlearning method is proposed to efficiently remove a subset of a client's training data from the federated learning model for human activity recognition, without compromising the model's performance on the remaining data.