  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 eliminates the necessity
for brief post fine-tuning, unlike conventional amnesiac unlearning. Moreover,
employing layer-wise partial updates in label-flipping and optimization-based
unlearning techniques demonstrates superiority in preserving model efficacy
compared to their naive counterparts.
