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Efficient Knowledge Deletion from Trained Models through Layer-wise Partial Machine Unlearning by Gogineni, Vinay Chakravarthi; Nadimi, Esmaeil S. is a scholarly article available to read on EtoBox.

What is Efficient Knowledge Deletion from Trained Models through Layer-wise Partial Machine Unlearning about?

Machine unlearning has garnered significant attention due to its ability to selectively erase knowledge obtained from specific training data samples in an already trained machine learning model. This capability enables data holders to adhere strictly to data protection regulations. However, existing unlearning techniques face practical constraints, often causing performance degradation, demanding brief fine-tuning post unlearning, and requiring significant storage. In response, this paper introduces a novel class of machine unlearning algorithms. First method is partial amnesiac unlearning, integration of layer-wise pruning with amnesiac unlearning. In this method, updates made to the model during training are pruned and stored, subsequently used to forget specific data from trained model. The second method assimilates layer-wise partial-updates into label-flipping and optimization-based unlearning to mitigate the adverse effects of data deletion on model efficacy. Through a detailed experimental evaluation, we showcase the effectiveness of proposed unlearning methods. Experimental results highlight that the partial amnesiac unlearning not only preserves model efficacy but also eli

Author

Gogineni, Vinay Chakravarthi; Nadimi, Esmaeil S.

Published

2024

Language

EN