Disposable Transfer Learning for Selective Source Task Unlearning

TL;DR

This paper introduces disposable transfer learning (DTL), a method that unlearns source task knowledge from a pre-trained model while preserving its performance on the target task, addressing privacy concerns.

Contribution

The paper proposes Gradient Collision loss (GC loss) for selective unlearning of source knowledge in transfer learning, enabling privacy-preserving model adaptation.

Findings

GC loss effectively unlearns source knowledge

Model retains target task performance after unlearning

PL accuracy decreases significantly with GC loss

Abstract

Transfer learning is widely used for training deep neural networks (DNN) for building a powerful representation. Even after the pre-trained model is adapted for the target task, the representation performance of the feature extractor is retained to some extent. As the performance of the pre-trained model can be considered the private property of the owner, it is natural to seek the exclusive right of the generalized performance of the pre-trained weight. To address this issue, we suggest a new paradigm of transfer learning called disposable transfer learning (DTL), which disposes of only the source task without degrading the performance of the target task. To achieve knowledge disposal, we propose a novel loss named Gradient Collision loss (GC loss). GC loss selectively unlearns the source knowledge by leading the gradient vectors of mini-batches in different directions. Whether the model successfully unlearns the source task is measured by piggyback learning accuracy (PL accuracy). PL accuracy estimates the vulnerability of knowledge leakage by retraining the scrubbed model on a subset of source data or new downstream data. We demonstrate that GC loss is an effective approach to the DTL problem by showing that the model trained with GC loss retains the performance on the target task with a significantly reduced PL accuracy.