SCU：一种用于深度学习赋能的语义通信的高效机器遗忘方案
SCU: An Efficient Machine Unlearning Scheme for Deep Learning Enabled Semantic Communications
AI总结：
针对深度学习赋能的语义通信中用户数据擦除需求，提出SCU方案，通过最小化互信息实现编解码器联合遗忘，并采用对比补偿方法恢复模型效用，在三个数据集上验证了有效性与效率。
摘要：
深度学习赋能的语义通信利用深度学习训练编码器和解码器（编解码器）以提取和恢复语义信息。然而，大多数语义训练数据集包含个人隐私信息。当之前的用户希望将其数据从语义系统中移除时，此类担忧引发了对从语义编解码器中指定数据擦除的巨大需求。现有的机器遗忘解决方案从训练好的模型中移除数据贡献，但通常仅限于监督式单一模型场景。这些方法在语义通信中不可行，因为语义通信通常需要联合训练无监督的编码器和解码器。在本文中，我们研究了深度学习赋能的语义通信中的遗忘问题，并提出了一种语义通信遗忘（SCU）方案来解决该问题。SCU包含两个关键组成部分。首先，我们通过最小化学习到的语义表示与被擦除样本之间的互信息，为语义编解码器（包括编码器和解码器）定制联合遗忘方法。其次，为补偿遗忘导致的语义模型效用下降，我们提出了一种对比补偿方法，该方法将被擦除数据视为负样本，将剩余数据视为正样本，以对比方式重新训练被遗忘的语义模型。在三个代表性数据集上的理论分析和大量实验结果证明了我们提出方法的有效性和效率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
