  Machine unlearning is the process of efficiently removing the influence of a
training data instance from a trained machine learning model without retraining
it from scratch. A popular subclass of unlearning approaches is exact machine
unlearning, which focuses on techniques that explicitly guarantee the removal
of the influence of a data instance from a model. Exact unlearning approaches
use a machine learning model in which individual components are trained on
disjoint subsets of the data. During deletion, exact unlearning approaches only
retrain the affected components rather than the entire model. While existing
approaches reduce retraining costs, it can still be expensive for an
organization to retrain a model component as it requires halting a system in
production, which leads to service failure and adversely impacts customers. To
address these challenges, we introduce an exact unlearning framework --
Sequence-aware Sharded Sliced Training (S3T), designed to enhance the deletion
capabilities of an exact unlearning system while minimizing the impact on
model's performance. At the core of S3T, we utilize a lightweight
parameter-efficient fine-tuning approach that enables parameter isolation by
sequentially training layers with disjoint data slices. This enables efficient
unlearning by simply deactivating the layers affected by data deletion.
Furthermore, to reduce the retraining cost and improve model performance, we
train the model on multiple data sequences, which allows S3T to handle an
increased number of deletion requests. Both theoretically and empirically, we
demonstrate that S3T attains superior deletion capabilities and enhanced
performance compared to baselines across a wide range of settings.
