面向上行链路干扰消除的机器遗忘
Machine Unlearning for Uplink Interference Cancellation
AI总结：
本文提出将机器遗忘（MUL）用于AI无线系统的上行链路干扰消除，无需重新训练或数据清洗即可净化受损潜在空间，并在分类任务中实现30%的准确率提升。
摘要：
机器遗忘（MUL）被引入作为一种在人工智能（AI）使能的无线系统中实现干扰消除的手段。观察到，在存在受损AI模型的情况下，采用MUL进行干扰消除在分类任务准确率上展现出30%的提升。相应地，消除了对现有干扰源瞬时信道状态信息的需求，并且利用MUL算法净化了带有干扰噪声的受损潜在空间，在无需重新训练或数据集清洗的情况下实现了这一目标。成员推理攻击（MIA）作为基准，用于评估MUL在神经网络模型中缓解干扰的有效性。通过评估干扰概率和需要重新训练的样本数量来确定MUL算法的优势。在一个简单的信噪比分类任务中，各种测试用例在准确率方面的综合提升表明，MUL展现出广泛的能力和局限性，尤其是在原生AI应用中。
英文摘要：
Machine unlearning (MUL) is introduced as a means to achieve interference cancellation within artificial intelligence (AI)-enabled wireless systems. It is observed that interference cancellation with MUL demonstrates $30\%$ improvement in a classification task accuracy in the presence of a corrupted AI model. Accordingly, the necessity for instantaneous channel state information for existing interference source is eliminated and a corrupted latent space with interference noise is cleansed with MUL algorithm, achieving this without the necessity for either retraining or dataset cleansing. A Membership Inference Attack (MIA) served as a benchmark for assessing the efficacy of MUL in mitigating interference within a neural network model. The advantage of the MUL algorithm was determined by evaluating both the probability of interference and the quantity of samples requiring retraining. In a simple signal-to-noise ratio classification task, the comprehensive improvement across various test cases in terms of accuracy demonstrates that MUL exhibits extensive capabilities and limitations, particularly in native AI applications.
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。
