SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications

TL;DR

This paper introduces SCU, an efficient unlearning scheme for deep learning-based semantic communication systems, enabling data erasure while maintaining model utility through mutual information minimization and contrastive compensation.

Contribution

The paper proposes a novel semantic communication unlearning (SCU) scheme that jointly unlearns data from encoders and decoders and compensates for utility loss using contrastive methods.

Findings

SCU effectively removes data influence from semantic codecs.

The contrastive compensation improves model utility after unlearning.

Experimental results show SCU's efficiency and effectiveness on multiple datasets.

Abstract

Deep learning (DL) enabled semantic communications leverage DL to train encoders and decoders (codecs) to extract and recover semantic information. However, most semantic training datasets contain personal private information. Such concerns call for enormous requirements for specified data erasure from semantic codecs when previous users hope to move their data from the semantic system. {Existing machine unlearning solutions remove data contribution from trained models, yet usually in supervised sole model scenarios. These methods are infeasible in semantic communications that often need to jointly train unsupervised encoders and decoders.} In this paper, we investigate the unlearning problem in DL-enabled semantic communications and propose a semantic communication unlearning (SCU) scheme to tackle the problem. {SCU includes two key components. Firstly,} we customize the joint unlearning method for semantic codecs, including the encoder and decoder, by minimizing mutual information between the learned semantic representation and the erased samples. {Secondly,} to compensate for semantic model utility degradation caused by unlearning, we propose a contrastive compensation method, which considers the erased data as the negative samples and the remaining data as the positive samples to retrain the unlearned semantic models contrastively. Theoretical analysis and extensive experimental results on three representative datasets demonstrate the effectiveness and efficiency of our proposed methods.