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Machine Unlearning in Contrastive Learning
Machine unlearning is a complex process that necessitates the model to diminish the influence of the training data while keeping the loss of accuracy to a minimum.
Data Trading Combination Auction Mechanism based on the Exponential Mechanism
Different buyers have varying degrees of demand for various types of data, and auctions play a role in such scenarios due to their authenticity and fairness.
Federated Unlearning for Human Activity Recognition
In response, we propose a lightweight machine unlearning method for refining the FL HAR model by selectively removing a portion of a client's training data.
Machine unlearning via GAN
Machine learning models, especially deep models, may unintentionally remember information about their training data.
Lightweight machine unlearning in neural network
Our method only needs to make a small perturbation of the weight of the target model and make it iterate in the direction of the model trained with the remaining data subset until the contribution of the unlearning data to the model is completely eliminated.
