  We present Synergy Aware Forgetting Ensemble (SAFE), a method to adapt large
models on a diverse collection of data while minimizing the expected cost to
remove the influence of training samples from the trained model. This process,
also known as selective forgetting or unlearning, is often conducted by
partitioning a dataset into shards, training fully independent models on each,
then ensembling the resulting models. Increasing the number of shards reduces
the expected cost to forget but at the same time it increases inference cost
and reduces the final accuracy of the model since synergistic information
between samples is lost during the independent model training. Rather than
treating each shard as independent, SAFE introduces the notion of a shard
graph, which allows incorporating limited information from other shards during
training, trading off a modest increase in expected forgetting cost with a
significant increase in accuracy, all while still attaining complete removal of
residual influence after forgetting. SAFE uses a lightweight system of adapters
which can be trained while reusing most of the computations. This allows SAFE
to be trained on shards an order-of-magnitude smaller than current
state-of-the-art methods (thus reducing the forgetting costs) while also
maintaining high accuracy, as we demonstrate empirically on fine-grained
computer vision datasets.
