使用遗忘神经网络进行机器无学习
Machine Unlearning using Forgetting Neural Networks
AI总结：
本文提出基于遗忘神经网络的无学习方法，通过乘法衰减因子显式编码遗忘，有效移除与训练数据相关的信息，同时保持模型性能。
摘要：
现代计算机系统存储大量个人数据，推动了人工智能和机器学习的发展，但也可能威胁用户隐私和信任。出于隐私考虑，有时希望机器学习模型能够忘记其训练过程中的一部分数据。在本文中，我们介绍了一种基于遗忘神经网络（FNNs）的新无学习方法，这是一种受神经科学启发的架构，通过乘法衰减因子显式编码遗忘。尽管FNNs之前已被研究为理论构造，我们提供了首个具体的实现，并展示了其在目标无学习中的有效性。我们提出了几种变体，包括每神经元的遗忘因子，包括基于激活水平的秩分配，并在MNIST和Fashion-MNIST基准上进行了评估。我们的方法系统地移除与遗忘集相关的信息，同时在保留的数据上保持性能。成员推断攻击证实了基于FNN的无学习在擦除神经网络中与训练数据相关的信息的有效性。这些结果确立了FNNs作为高效且可解释无学习基础的前景。
英文摘要：
Modern computer systems store vast amounts of personal data, enabling advances in AI and ML but risking user privacy and trust. For privacy reasons, it is sometimes desired for an ML model to forget part of the data it was trained on. In this paper, we introduce a novel unlearning approach based on Forgetting Neural Networks (FNNs), a neuroscience-inspired architecture that explicitly encodes forgetting through multiplicative decay factors. While FNNs had previously been studied as a theoretical construct, we provide the first concrete implementation and demonstrate their effectiveness for targeted unlearning. We propose several variants with per-neuron forgetting factors, including rank-based assignments guided by activation levels, and evaluate them on MNIST and Fashion-MNIST benchmarks. Our method systematically removes information associated with forget sets while preserving performance on retained data. Membership inference attacks confirm the effectiveness of FNN-based unlearning in erasing information about the training data from the neural network. These results establish FNNs as a promising foundation for efficient and interpretable unlearning.
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。
